Science and Communication
Summary and Keywords
Whether understood as a set of procedures, statements, or institutions, the scope and character of science has changed through time and area of investigation. The prominent current definition of science as systematic efforts to understand the world on the basis of empirical evidence entails several characteristics, each of which has been deeply investigated by multidisciplinary scholars in science studies. The aptness of these characteristics as defining elements of science has been examined both in terms of their sufficiency as normative ideals and with regard to their fit as empirical descriptors of the actual practices of science. These putative characteristics include a set of commitments to (1) the goal of developing maximally general, empirically based explanations certified through falsification procedures, predictive power, and/or fruitfulness and application, (2) meta-methodologies of hypothesis testing and quantification, and (3) relational norms including communalism, universalism, disinterestedness, organized skepticism, and originality. The scope of scientific practice has been most frequently identified with experimentation, observation, and modeling. However, data mining has recently been added to the scientific repertoire, and genres of communication and argumentation have always been an unrecognized but necessary component of scientific practices. The institutional home of science has also changed through time. The dominant model of the past three centuries has housed science predominantly in universities. However, science is arguably moving toward a “post-academic” era.
At the beginning of the 21st century, English-speaking scientific organizations have come to define science as systematic efforts to understand the world on the basis of empirical evidence (American Association for the Advancement of Science, 1989; Science Council, UK, n.d.). In spite of rough agreement among such practitioners and promoters of science, there is little consensus among scholars who take science itself as an object of study about whether science exists as an entity with stable and distinctive features and, if so, what those features might be. Some philosophers of science insist that science is a “natural kind” (Franklin, 2009, p. 62), while others have argued that “The events, procedures and results that constitute the sciences have no common structure; there are no elements that occur in every scientific investigation but are missing elsewhere” (Feyerabend, 2010, p. xvix; Bauer, 1992) (see Figure 1). Scholars from multiple other disciplines, including rhetoric and communication studies, feminism, sociology, and history, have also explored the multiple facets of science, employing varying assumptions about its core features and value.
Variations in definitions of science arise from the different possible usages of the term, including the tendency to refer to the noun “science” as a set of institutions, a set of procedures, or as a body of statements taken to constitute knowledge grounded through a set of particular criteria. Additionally, some definitions seek to be descriptive, while others are normative. The subjects addressed by scientists and scientific institutions also vary enormously from the quantized behavior of subatomic particles to the history of the universe or the behavior of symbol-using animals with complex and commonly conflicting motivations (Figure 1). The methods employed within scientific institutions to contribute to scientific knowledge have consequently also varied through time and subject matter, having included systematic and comparative observation, controlled or planned experiment, model building, and data mining (Figure 1). Sections overview key perspectives on the various components that have been seen as constitutive of science among multidisciplinary treatments, herein organized by the categories of central commitments, practices, and institutions typical of phenomena or activities widely classified as “scientific.”
Theorists of science, including scientists and scientific organizations, have identified a variable set of goals, methodologies (hypo-deductive, quantitative), and relational norms as widely shared commitments among scientists that should give (the prescriptive focus), or actually do give (the descriptive focus), a distinctive shape to the scientific enterprise.
Scientific goals are generally described as the search for understanding or explanation, but sometimes they are also described in terms of technologies or applications. Key ways in which the kinds of understanding that science seeks have been specified include generality, empiricism, falsifiability, and prediction.
The kind of explanation widely viewed as typical or requisite for scientific accounts includes a maximizing of generality that is nonetheless based in local and concrete or empirical conditions. Robert Ackerman (1970, p. 41), a philosopher of science, articulated this vision of understanding as:
the pervasive development of highly abstract models in a suitable symbolism which are initially proposed to fit conceptually simplified cases, and which are then gradually made more sophisticated by relaxing constraints to obtain more general models until a comprehensive scope of explanation of data is finally obtained.
Some science studies scholars have critiqued the ideal of seeking maximally universal explanations. They argue that “universal” explanations are either impossible or destined to suppress or ignore important variations, and that false universals contribute to political oppression (Harding, 1986; Martin, 1991). As a matter of historical fact, a unified “theory of everything” has not been achieved, even in physics. Crucially, the level of generality achievable in physics appears to be greater than other areas of investigation because physics engages in a tautology: it limits itself to those phenomena that are time- and space-invariant and to a relatively small number of relatively self-identical objects and forces. Physics is thus atypical of topic matters for scientific research rather than providing a model of science in general. For example, biology presumes a time-dependent elaboration of forms of being (evolution) and engages a massively greater number of types (e.g., over 8 million species on earth), which are commonly discriminated from each other not as “natural kinds,” but by progressive, quantitative variation (e.g., microbial species are generally distinguished from each other by percentage of DNA variation rather than by specific physical differences). Biology presumes an open future (new categories and functional options will evolve), whereas physics presumes no new natural laws will develop. Studies of human behavior similarly presume that new categories of behaviors will develop with the continued elaboration of human symbolic space. Although some generalizations can be derived that may apply to all biological beings or to all human beings, limiting scientific study to only those areas that characterize all beings would severely curtail the scope of understanding and applicability of scientific knowledge. A definition of the shared components in the category “tree” does not tell one everything that is of interest to know about all of the kinds of trees.
These criticisms indicate that casting generality in terms of “universality” is inappropriate to the most full or useful description of scientific aspiration. However, this does not invalidate the attempt to ascertain a maximum generality. In practice, the effort to expand the bounds of a scientific concept or explanation as far as possible appears to be part of the process by which the limits of such concepts can be discovered (e.g., the overstatements about the universal causal potency of genes of the late 20th century failed, and in so doing illuminated the role of epigenetic forces that were previously not understood). Uncovering the specific limits of a generalization may also provide an effective means of illuminating the material differences among phenomena (e.g., the failure in human trials of medical products that were successful in animal trials provides pointers to biochemical differences in the tested species). Linking the ideal of generality to the ideal of empirical fit in specific cases may thus forward the process of producing scientific explanations.
Most characterizations of science emphasize its grounding in “observable phenomena” as a means of drawing a contrast to explanatory systems that are grounded primarily in transcendent realms of being (e.g., theology) or in normative ideals (e.g., ethics or ideology). This focus can be described as “empiricism.” Early models of empiricism have been repeatedly and definitively criticized for failing to attend to the way that observations are shaped by the active role of human perception and belief systems, as well as the limits of observational tools (O’Keefe, 1975, pp. 174–175). These appropriate criticisms, however, do not demonstrate the opposite conclusion, that is, that the results of observation are solely the product of human perceptual apparatuses, belief systems, or observational tools. Studies of particular scientific endeavors have highlighted the resistance of variables external to those three elements, and it is not unreasonable to characterize those sources of resistance as “the natural,” “materiality,” or “reality.” Pickering’s (1995, p. 22) empirically informed description of scientific practices provides a middle ground approach. It characterizes the scientist’s research path as a “mangle of practice” constituted by a constant series of “accommodations” to the “resistances” provided by sources external to what the scientist or scientific community brings to bear.
Science skeptics have also suggested that the claim to an empirical component of science is demonstrably nullified by the changes across time in what have been taken to be well-demonstrated scientific conclusions. Such changes, however, do not demonstrate that scientific statements are “nothing but ideology.” For example, when skeptics portray Einstein’s theory of relativity as overthrowing Newtonian science, defenders reply that Einstein’s theories merely placed a larger edifice around a still-applicable Newtonian arena.
A middle-ground position nonetheless recognizes that factors such as the complexity of material realities, the material limits constituted by human perceptual systems, the inherently limited capacities of any tool to detect preferentially what it is designed to assess, and the power of existing human beliefs to guide the path of inquiry in fact typically produce substantial tendencies to repeat “bad science” in many areas (e.g., the continued adherence to totalistic models of genetic causation by some natural and social scientists, or the slow reframing of gender-based errors, Condit, 1996). Rather than delegitimating science, the power that ideological frames have demonstrated to divert or slow progress or turnover in scientific explanations instead warrants the inclusion of critical reflection on beliefs, tools, and perceptual tendencies as a core of scientific practice itself (Harding, 1986; Depew & Lyne, 2013).
One way to re-characterize empiricism in light of the now well-demonstrated role of human perceptual and conceptual inputs is to describe empirical observation in science as an “inter-subjective” practice rather than an objective one (Ziman, 1978, p. 7). Objectivity is generally taken to mean the effort to subtract all (particular) human beliefs, passions, or interests (Ackerman, 1970, p. 58). In contrast, the concept of inter-subjectivity utilizes the differences in biases among humans as a resource for correcting for interests and perceptions without presuming that any human, or group of humans, could be free of biases. Differences of perception become a resource for examining the variables and conditions that produce a particular perception. If a peak at a particular point in an X-ray diffraction spectrum cannot be recognized by people of all possible backgrounds, then the identification of the peak must be treated potentially as an illusion or a distortion resulting from the perceptual or ideological biases of those who see it. This view of the advantage of science as resting in the intersubjectivity of its practice is complicated but not vitiated by the extensive training required for most scientific observation (Chalmers, 2013, p. 7). The grounding of science in intersubjectivity entails the conclusion that scientific institutions that promote the inclusion of people from multiple backgrounds are better situated to achieve the goals of science. The inclusion of women in science in the past decades has illustrated this effect empirically, as the inclusion of feminist perspectives broadened medical research to include female bodies in research, and the inclusion of women in biology has vastly expanded the array of hypotheses available for testing various evolutionary theories, and some of these hypotheses have subsequently garnered substantial empirical support (Schiebinger, 2000).
An alternate characterization of the goal of science that avoided these and other issues was the proposal by Karl Popper (1959) that science could only be understood as focused on the destruction of misconception rather than on the creation of knowledge. Based in the Humean analysis of the impossibility of grounding induction in an absolutely securable premise, Popper argued that what science could do uniquely and reliably was falsify hypotheses. Popper’s approach gained numerous adherents, but it fails to describe the history and practices of science. Scientists do not simply discard hypotheses; they engage in the building of paradigms and treat large bodies of knowledge as relatively settled, even if they are also “in principle” committed to the potential for any previous hypothesis or paradigm to be overturned. The failure to falsify a hypothesis is widely treated in actual scientific practices as the basis for including the theoretical statement from which it derives as part of the knowledge structure (under the proper conditions, which may include factors such as replication, balance of competing hypotheses, boundaries of the research community, etc.). These factors were brought to the foreground by Thomas Kuhn’s (1962) emphasis on the overturning of scientific paradigms, which gained widespread attention. Kuhn’s account contributed a description of science as including both the building up of paradigms and their replacement, although Kuhn did not provide an empirical survey that could justify the characterization of science as primarily or focally a practice of overturning paradigms as opposed to accreting or envisioning and supporting them.
Many descriptive and normative accounts of science emphasize the ability to predict the outcomes of experiments and structured observations as a defining goal of science (Franklin, 2009, p. 21). Cases of successful prediction often provide central vignettes in public accounts of science or scientific progress (e.g., the prediction of gravitational lensing). These exemplars tend to presume that the norm in scientific progress is the single “crucial experiment,” the outcome of which definitively decides between competing theories. Critics have noted, however, that consensus about what counts as a successful prediction depends on a variety of factors, because experiments may fail for a variety of reasons other than a lack of fit of the empirical behavior with the prediction (Chalmers, 2013).
An additional critique of the centrality of predictability is the argument that the ability to predict is irrelevant, pragmatically unavailable, vague, or trivial in many areas of scientific endeavor or at crucial periods of scientific progress in some areas. The often distant link between higher-order theories and local cause-effect relationships explains the basis for the inapplicability of prediction as a criterion in some scientific endeavors. In areas more complex than physics (e.g., climatology, geology, some areas of biology, and human behavior), theoretical structures account for multi-variable configurations that are time-dependent, and no single experiment can account for multiple variables. Typically, such theoretical structures are supported by multiple kinds of evidence, rather than solely by a singular experiment. Thus, for example, evolutionary theory has been certified by no single “crucial experiment” or predictive capacity, but rather is anchored by the gradual accumulation of evidence that has turned out to be consistent with that theory, when it might have been otherwise the case in each instance (e.g., the genetic basis of all living beings on earth has turned out to be tied to RNA/DNA; organisms that were historically classified as related have turned out for the most part to have traceable, regular patterns of change in their DNA sequences; the mechanics of DNA/RNA/proteins turn out to link to consonant patterns of physiological similarities and differences). Prediction thus might be seen as a desirable and important component of some kinds of scientific practice, but to centralize it as the sole component of science would eliminate from the definition of science much of what is currently generally understood as scientific practice.
Applications and Fruitfulness
The practical utility of a scientific theory or paradigm provides an alternative goal for science, both due to its extrinsic benefits and due to its ability to provide an alternative test for the fruitfulness of theories. There is little agreement about whether technological or other kinds of applications (e.g., psychological therapies, pedagogical prescriptions, and social policies) form an integral goal or component of science or are merely an external consequence. As a matter of practice, the development of technologies has been integrally tied to scientific progress in much of science’s history (at least through the development of scientific equipment to perform studies including microscopes, telescopes, and genetic sequencers), but this is not universal.
Applications of science also have ongoing pragmatic relevance to the scope and directions of science. A science that did not produce valued technological applications likely would gain far less public financial support and thus as a practical matter would not be as broad and deep a social institution and would not progress as rapidly. Additionally, success and richness in applications may draw scientists to an area. Thus, applicability to human interests and problems serves de facto as a consequential component of scientific practice.
For some analysts, however, a criterion of a scientific theory is its fruitfulness. This may be judged merely by the ability of a theory to generate additional hypotheses or to account for a wider range of data. However, the generation of applications may also be taken to provide a measure of the fruitfulness of a theory or research paradigm.
In addition to goals, science has been characterized as entailing commitments to meta-theories that describe the general approaches by which science gets accomplished. The hypo-deductive approach and quantification are the most frequently identified of these practices.
Even if one discounts Popper’s view that the goal of science is merely falsification, his portrayal of the hypo-deductive method as a key component of science has maintained widespread resonance. The hypo-deductive method specifies that a scientific endeavor involves formulating a testable hypothesis and testing it. Popper labeled the procedure “deductive” because he presumed that whether the hypothesis should be accepted or rejected could be rigorously and certainly ascertained according to deductive logic, in light of evidence provided by the test. In practice, it has turned out that statistical (and hence probabilistic) assessment is more typically required because most natural phenomena occur in distributions rather than in self-identical natural kinds (though violations of deductive logic would also disqualify the conclusion one made from a test). It has thus not proven appropriate to try to cleanse scientific reasoning of induction in favor of pure deduction.
The renaming of the “hypo-deductive method” as “hypothesis testing” better signals the inclusion of probabilistic standards and the constructive rather than eliminative goal of science. In this broader conception of the over-arching approach of science, the criterion of replicability—that independent sources are able to produce the same results using the same and related tests—has served as an important augmentation of falsifiability, and the importance of actual replication, rather than merely “in principle” replication, has recently been highlighted (Open Science Collaboration, 2015).
When treated as a component of the methodology specified as hypothesis testing (rather than as the central goal of science), the criterion of falsifiability specifies an important set of constraints on the symbolic practices required for conducting science. As Ziman (1978) described it, effective hypotheses must be “consensible”—that is, clear and specific enough that there can be inter-subjective agreement on what would count as falsification. Early efforts to specify consensibility purely as “operational terms” have been appropriately discounted (O’Keefe, 1975). The need to link the test of a specific hypothesis to a theoretical structure of greater generality and to other potential tests creates insuperable challenges for defining “consensible” on the basis solely of operationalization (though the obverse may be relevant: if a hypothesis cannot be operationalized, it cannot be scientifically tested). There are no supra-disciplinary formulae for specifying how to frame a consensible hypothesis. The assessment of consensibility is one of the key places where science remains, and must remain, a practice guided by the norms and constraint of argumentation rather than by mathematics or experiment (see following).
Some scientists and theorists have maintained that quantification is a necessary and distinguishing feature of science (as compared to other academic pursuits such as humanistic inquiry) (Franklin, 2009, p. 3; Hong, 2004). To the extent that quantification enables precise formulation of criteria for rejecting hypotheses, there can be little doubt that quantification has been highly influential in the history of science, attaining even “near ubiquity” (Porter, 1995). However, some facts and even tests of hypotheses are not fundamentally quantitative. The growth of any of a test bacterium on a petri plate impregnated with amoxicillin indicates that the amoxicillin-resistant gene has been successfully incorporated into the original strain of the bacterium. No counting is required when mere presence provides the criterial basis for the success of an experiment. The discovery of a single plant that used a silicon rather than carbon-based nucleic acid would establish an entire new line of biological investigation. In practice, quantification is highly important, but non-quantitative work has also provided a component of scientific efforts.
Conversely, many nonscientific efforts now rely heavily on quantitation (financial accounting, numbering systems for government documents, regulatory oversight systems), so quantification does not demarcate science from other contemporary practices. Quantification also does not distinguish natural from human sciences. Because most human behaviors occur in distributions rather than as obligately linked to natural kinds, mere tests of the presence or occurrence of particular characteristics will typically fail to accurately characterize the human repertoire. Further, because most human behaviors have multiple, complex, degree-sensitive inputs that are developmentally contingent, quantitative assessments need to be extraordinarily complex as well. Contemporary social science is thus by no means comparatively “soft” in quantification, even if observational (“qualitative”) studies continue to play a visible role in the research processes of the social sciences as they have in astronomy, geology, climatology, and the other synthetic sciences.
C. P. Snow (1959) famously argued that science and the humanities were distinguishable as two different “cultures” of inquiry, harboring distinctive attitudes that are instrumental in producing and maintaining different bases of knowledge. Although many scientists may not be consciously attentive to attitudes or norms, observers of scientific practice have posited that scientific belief systems are importantly shaped by a set of social norms. In a pioneering and influential study, the sociologist Robert K. Merton (1973) identified four social norms as integral to the scientific belief system and therefore as structuring the activities of science. First, he defined “universalism” as dictating that truth-claims “are to be subjected to preestablished impersonal criteria,” which meant that the inclusion or rejection of a claim was “not to depend on the personal or social attributes of their protagonist; his race, nationality, religion, class, and personal qualities as such are irrelevant” (p. 270). He indicated that scientific practice was also guided by a norm of “communism” (later renamed “communalism” to avoid political and economic connotations) because the “substantive findings of science are a product of social collaboration and are assigned to the community” (p. 273). Third, science manifested a “disinterestedness,” which was not to be confused with altruism, but rather “has a firm basis in the public and testable character of science” (p. 275). Finally, he identified science as manifesting “organized skepticism,” which was “both a methodological and an institutional mandate” defined by “the temporary suspension of judgment and the detached scrutiny of beliefs in terms of empirical and logical criteria” (p. 277). A fifth norm of “originality” was later added (Anderson, Ronning, DeVries, & Martinson, 2010). Subsequent empirical studies of the presence of these norms in scientific practice have indicated not only that there is cultural variability in alignment with these norms, but also substantial ambivalence, and greater complexity of the normative structure (Anderson & Louis, 1994; Anderson et al., 2010; Mitroff, 1974). Others have highlighted difficulties involved in the concept of norms and their assessment (Mulkay, 1980).
An alternative to defining science in terms of the beliefs or ideals that guide it is instead to characterize its typical practices. The observable, day-to-day practices of scientists are, however, enormously variable. They include for geneticists the pipetting of fluids, insertion of tubes into machines, and imaging of gels, for geologists the tramping across mountains and into caves to make observations and collect samples, for astronomers the sustained scanning of computer screens linked to telescopes, and for neuropsychologists the questioning of humans in fMRI (functional magnetic resonance imaging) machines to point out only a few possibilities (even within these four fields). These highly diverse practices are all recognized as participating in “science” because they are classifiable as fitting within one of several approaches to the invention or discovery of hypotheses and their testing. These practices have been most frequently categorized as experimentation, observation, simulation or modeling, and data mining. For historical reasons, the standing of communication, including argumentation, as a core practice of science is less widely recognized.
Many resources link science to the use of experimentation or even identify it with the use of “the experimental method” (Franklin, 2009, p. 21). In the folklore about science, experiments provide the sharp line demarcating “science” from pre-scientific or nonscientific activities. There can be little doubt that experimentation constitutes a major and important activity in endeavors constituted as scientific. However, accounts that presume experimentation to be the sole productive activity in the history and contemporary practice of science either overlook crucial non-experimental episodes or define experiment so broadly as to encompass model-building and observation. To list only a few examples, the Copernican Revolution rested on model-building and systematic observation rather than experimentation, the research for which Watson and Crick were awarded the Nobel Prize was performed via the fitting of observational data to a novel model, and the theory of plate tectonics resulted from multiple observations. The tendency to overlook the critical role of non-experimental practices in science arises in part from the vagueness of definitions of experiment.
There does not seem to be a consensus definition of “experiment,” but there are several frequently identified elements of these definitions: linkage to hypothesis testing, deliberate manipulation, establishment of controlled conditions, and appropriate recording of results. Experiments are usually characterized as being performed to test a hypothesis, although they are also linked to replicating results or confirming knowledge (Open Science Collaboration, 2015), testing or assessing efficacy, as well as to discovering novel information (Debru, 2013). As discussed previously, analysts have shown that the linkage between hypotheses and any particular set of actions typically makes any single “test” partial and non-definitive, so “contribute to the testing of” or “contribute to the assessment of” a hypothesis might be more accurate than “to test a hypothesis.” When the relationship between a precisely formulated hypothesis and its test is specific enough, a contrary result will disprove the hypothesis, but in many cases the sufficiency of the relationship is disputable.
In order to enable an appropriate test of a hypothesis in any situation where multiple forces or causes might influence an outcome, it is logically requisite to “control” or “control for” these multiple potential influences. In the idealized form, the concept of control means designing an experiment so that only one potential influence may be active in producing the outcome (Chalmers, 2013). Because isolating all variables is typically impossible in complex, large-scale phenomenon, the use of statistical methods to try to “control for” other influences has been widely employed. Another approach involves two kinds of reductionism: reduction to parts and the substituting of laboratory for field conditions. The former approach assumes that pieces of a complex can be removed from the complex and they will maintain their fundamental features; a small piece of a complex can be tested with greater control than the whole. Understanding the relationship between two cogs is more manageable than trying to understand an entire factory in a single study. In many instances, this theoretical assumption justifies laboratory research in place of more complex field conditions.
The validity of the assumptions of such reductionism, however, is widely debated (Ahn, Tewari, Poon, & Phillips,2006; Gallagher & Appenzeller, 1999). If one removes a heart and lung from a living being, it dies, so that it is impossible to study the interaction of the heart and lung independent of the brain and other components of the body, which means that in medicine the simplest of reductionist approaches fails. But relatively simple reductionist approaches have proven enormously useful in some instances (e.g., they have provided the basis of several fruitful research projects employing vacuum chambers in physics). And reductive research can provide some information even to complex systems (chemical analyses of blood inform understanding of heart/lung function). The fruitfulness of reductionist approaches to heightening control is therefore topic-dependent. It also means that assessment of the accuracy of reductionist findings generated in laboratories should always be tentative in the absence of comparison to results of holistic studies, which for large-scale phenomena may mean field observations. There is conflict, however, about the relationship of field and laboratory studies (Jerit, Barabas, & Clifford, 2013).
The perceived need to ensure that the target or “independent variable” is “under the direct control of the researcher” (Keppel, 1991, p. 6) seems to be related to the perception that experiments occur only in a laboratory or at least when someone “manipulates” something in the world. However, there is a tendency to obfuscate the issue by employing the term “experiment” when identifying well-planned systematic observations in which the only “manipulation” involved is that required to make measurements or observations. The “World Ocean Circulation Experiment” provides an example of an international project that employed the descriptor “experiment,” but the actual practices involved were primarily observational and modeling.
In fields such as human medicine, correlational studies that draw from existing databases, rather than entailing novel manipulations by a scientist testing a specific hypothesis, are widely described as “experiments” (Keppel, 1991, p. 9). Such correlational studies have evident limitations, but due to ethical constraints, expense, and complexity, such studies may form a crucial component of the research process. To discredit their informative value because they fail some idealized model of an experiment (which is not available due to the real characteristics of the field of study) would be to decrease the available information for theory-formation rather than to increase it. The limited reach of experiment in the narrow sense has generated acceptance among the empirically informed that practices other than experiment as narrowly defined are integral to science.
All human beings are observers, so if observation is crucial to scientific practice, are there distinctive requirements for scientific observations? The distinction, like most others, seems to be a matter of degree. Careful or systematic observation of empirical phenomena has formed and continues to form a crucial part of scientific practices. Sir Francis Bacon’s (1855) role as the earliest deliberate fomenter of the scientific revolution was due to his enjoinment to systematic observation, not to any codification of experimental practice (which would come later, perhaps with Newton). The three important elements of scientific observation might be identified as consistency across observational conditions, precision, and focus upon data specifically relevant to addressing specific explanatory hypotheses.
Commonsense observations tend to presume the consistency of observation across conditions. However, given the extensive psychological research establishing the tendency of human observational foci to vary in response to context (Chalmers, 2013), if one’s observations are to contribute to scientific explanations that seek to extend across contexts, then it is necessary to attend deliberately to observational consistency. In the social sciences, tests of inter-rater reliability provide a widely used mechanism to ensure that measurements are inter-subjectively reliable (Krippendorff, 2004, p. 211). In natural sciences, calibration and calibration checks similarly stabilize observations across conditions.
The use of devices or practices for enhancing consistency of observation across conditions can also be related to the need for a level of precision that exceeds commonsense conditions. The demand for comparatively extreme levels of precision is often related to the role of quantification in hypotheses. However, precision also becomes a requirement merely to detect many phenomena that are not accessible to commonsense observation, whether these be Higgs bosons or modest-sized changes in attitudes.
The demand for both consistency and precision are also tied to the widespread tendency for scientists to build technological devices to augment observation. The prominent role of the invention of the telescope and microscope in the folklore of science iconizes the role of technologies, but the creation and validation of “scales” in the social sciences also constitute such a technology, even if scales remain conceptual or “paper and pencil” based devices. As Pickering (1995) observed, there is a back-and-forth flow between efforts to design technologies to address specific categories of hypotheses and the opening of new hypotheses that new technologies generate, but in both directions of flow, there is a linkage between hypotheses and the generation of observational technologies that identify “observation” in science as specifically scientific.
Many of these features of scientific observation—stability across conditions, technological augmentation, high precision—have become important to many “nonscientific” areas such as accounting, manufacturing, surveillance, and weapons design. In this sense, they do not immediately distinguish scientific observation from other practices of advanced industrial societies. One might argue either that science is a recursive generator of ever higher demands for these qualities of observation or that science as it has evolved is merely one distinctive element of an industrial society and so shares its observational demands.
In an important sense, virtually all scientific practices might be said to entail models (Bailer-Jones, 2009). To the extent that the goal of science is the generation of explanations, those explanations are symbolic phenomena, and the term “model” can be defined as a representation that selects and integrates key features of a phenomenon in a simpler or more understandable format. Thus, scientific theories are commonly described as models. However, the term “modeling” took on an additional definition in the late 20th century as computers made it possible to build relatively complex mathematical models of natural processes or systems and to test them against selected data through simulations. There is substantial debate about the proper role of such simulations in scientific practice. Enthusiasts argue that mathematical modeling “constitutes the third pillar of science and engineering, achieving the fulfillment of the two more traditional disciplines, which are theoretical analysis and experimentation” (Quarteroni, 2009). More sober analyses note the many failures of mathematical modeling in predicting future outcomes (Culshaw, 2006).
Regardless of such concerns, mathematical modeling has become a highly favored tool in multiple topic areas ranging from health communication to hydrology to climate science. In some fields at least, the uncertain reliability of modeling with regard to predictive success appears to have been adapted to by the use of modeling as a recursive inventional tool; the inadequacy of predictions in an early-stage model becomes the basis for progressive model refinements. The model thus often becomes a means for exploring the role of different potential variables in different combinations rather than providing a final description capable of definitive prediction across all conditions (Oreskes, Shrader-Frechette, & Belitz, 1994).
The amplification of computing power that occurred at the end of the 20th century gave rise to an interest in what is colloquially called “Big Data.” Big Data projects typically involve mathematical modeling. Increasingly at the turn to the 21st century, however, advances in computing capacity enabled the use of computers in a fashion that involved little more than what has been disparagingly referred to as “dredging” for correlational relationships. In medical genetics, for example, instead of formulating relatively precise models—that is, hypotheses—about the involvement of specific genes with known functional characteristics, some investigators began using correlational approaches to “hunt” among large pools of genes for a gene or gene set that was correlated with a specific medical condition. Even though such studies often included mathematical corrections for the implicit multiple-testing biases, they were highly controversial precisely because they violated the fundamental logics of hypothesis testing and offered no substantive corrections or alternatives for the range of biases that arise absent hypothesis testing (Allen, 2001).
The National Institutes of Health has placed substantial proportions of its funding behind data mining (Collins, 2014). However, data mining appears to be a contributor to the increasingly obvious statistical problems that currently undermine the reliability of large swathes of findings in multiple fields, especially in biomedicine (Ioannidis, 2005), a problem that has recently been empirically verified through replication tests in psychology (Open Science Collaboration, 2015).
Communication and Argumentation
Empirically speaking, communication forms an evident component of science. This empirical fact has, however, been ignored in many descriptions of scientific practices because the history of the development of science in Europe encouraged a search for features that distinguished science from earlier practices for developing and testing explanations.
In the 16th and 17th centuries, theorists of what would become science (but was still typically called natural philosophy) sought to distinguish their practices from the entrenched, purely discursively based practices of theology and philosophy. The rules of deductive logic had originally been identified and described by Aristotle in 350 bc (in the Prior Analytics). Broader practices to enhance the rigor of argumentation had developed gradually with the development of writing, and perhaps accelerated with the development of print media. These older, discursively based approaches to testing explanations were used in the universities, which tended initially to exclude the new scientific methods and to defend theories the newcomers challenged (such as the geocentric vs. heliocentric theory of the solar system). In the competitive context, it was tempting to define science as an alternative to argument rather than as an addition to argument. Such a formulation is incorrect, because science could not be practiced without symbolic systems of some kind (e.g., mathematics), and in fact, could not be practiced without natural language.
Where precise deduction is not available—and, as multiple analysts have shown, this is usually the case (Toulmin, 1958)—then the recursive process of deliberation is what enables refinement of answers to questions such as the fit of a hypothesis and its test, the adequacy of a calibration, the scope of anomalies facing a theory, the appropriate statistical standards, and the implications of various mathematically calculated effect sizes, ad infinitum. Various models of argumentation have been applied to analyses of some dimensions of science (Keränen, 2010; Whithaus, 2012; Wynn, 2009).
One can identify various genres of scientific communication, but one specific form—the research report—appears in multiple scientific fields and venues. Bazerman (1997) has traced the development of that form from the mid-17th to the 19th century in England. Today it is reflected in the presentation of four core, inter-related components that appear in scientific research reports across many fields (though different fields include additional elements and vary in their naming and emphasis among the components). The typical research report begins by framing the question or hypothesis addressed by an experiment, observation, or simulation within the community’s pre-existing knowledge structure or other contexts (such as clinical implications in some areas of biology or geographic location in many areas of geology). This section of the report can be terse or elaborate depending on disciplinary norms. The second section of the report describes, usually in substantial detail, the methods used to perform the research. The third section reports the results of the research, while the fourth section (which may be divided into subsections such as discussion and conclusion) interprets the results in terms of the hypothesis or research question and in terms of the larger paradigm. In some fields, these sections include implications for potential applications, limitations of the findings, or proposed next steps needed to provide additional clarification, verification, or development of concepts. Scientific communications of all genres are embedded in specific institutions.
At least since 1663, when the Royal Society of London for Improving Natural Knowledge received official recognition, scientists and scientific practices have been tied to, supported by, or embedded in institutions. Today, although there is substantial variation across the diverse fields of inquiry, multiple sets of institutions provide the funding and means of communication essential to large-scale science, as well as providing formal and informal regulation. Prominent among these are scientific societies, funders, government agencies, and universities, as well as industrial organizations. Meanwhile, at the smaller scale, the organization and operation of the distinctive scientific entity signified by the term “laboratory” have also continued to grow. Through time and across the globe, the specific character of these institutions has varied. Currently, there may be a shift in progress from an institutional model based in academic settings to a “post-academic” or industrial model, which also has implications for science policy.
Scientific Societies: Meetings and Journals
The Royal Society began with meetings among practicing scientists, which served not merely to disseminate the findings of a particular experimenter or laboratory, but to provide a place for active assessment and on-going revision of particular conclusions and for the generation of additional hypotheses, paradigms, and implications through the discussion process. In other words, the discussions were themselves an essential part of scientific practice. It is not surprising therefore that contemporary fields of science are commonly organized around one or more societies, which may be differentiated according to subfields, segregated between applied and basic science, differentiated by emphasis on research vs. pedagogy, identified by geographic region, or focused on areas of overlap among major disciplines. The Mertonian norm of communalism is related to the essential role such institutions play in the communicative constitution of science. Post-academic science closes off open public discussion of this sort through confidentiality agreements, publication embargoes, and intellectual property agreements (Hellström, Jacob, & Wenneberg, 2003; Rodriguez, 2007; Ziman, 1993), but though they limit the participants involved at various stages of research on particular phenomena, they do not close off communication completely.
Expanding the function of assessment and circulation, the publication of scientific journals added a recording function, and this has been a key activity of most scientific societies since the Royal Society published the first issue of Philosophical Transactions in 1665. Today there are hundreds of scientific societies and thousands of scientific journals being regularly published. Without these print-based media (which are now generally accessed “online”), science could not exist in anything like its present form. These journals have also been influenced by the capitalization trends of post-academic science. Many scientific societies have turned operation of their journals over to for-profit entities. In 2013, for example, the majority of peer-reviewed academic papers were published by five companies: Reed-Elsevier, Springer, Wiley-Blackwell, Taylor & Francis, and Sage (Larivière, Haustein, & Mongeon, 2015). The extent to which this commercialization affects the contents of published research has not yet been empirically and analytically explored in detail, but the greater concentration of commercial journals in the social sciences as compared to the natural sciences and humanities is suggestive and might enable empirical comparisons.
The practice of science requires resources, including human time, intellectual capital, access to valuable and sometimes scarce and ethically laden resources, as well as the costs of highly specialized tools. In the early years of “modern” science, individual patrons were crucial to the work of many scientists (Sarasohn, 1993). In the contemporary era, the major funders of science and technology are corporations and governments, the latter of which funnel funds through grants and contracts but also through the support of universities. Recent innovations such as crowd-sourcing (Bagla, 2012), citizen science (American Association for the Advancement of Science, 1989; Nov, Arazy, & Anderson, 2014), and prize competitions provide some support of some kinds of scientific projects, while a renewed interest exists in direct appeal to philanthropists (Ledford, 2012). Although there have been some small studies about the effects of funding sources and their structure and values upon the shape of science, there cannot be said to be definitive conclusions available among the shifting and internationally variable patterns (Holbig, 2014; Mukhopadhyay, 2014).
Universities and “Academic” Science
The bonding of faculty and students in a governmentally recognized entity that is the precursor of the contemporary university dates at least to the 12th century, but universities were not initially hospitable to the development of science (de Ridder-Symoens, 1996, p. 38). By the 19th century, however, the identity of science and of the university had become fused. Eventually, the methods and assumptions of science would dominate most universities, as the social sciences developed to apply the methods of science to human beings and even many of the humanities began to frame their primary undertaking as theory building (rather than as philosophy or criticism). Currently, a “post-academic” version of science may be developing (Hellström, Jacob, & Wenneberg, 2003). Scientific activities are increasingly located inside corporations, in independent or quasi-independent research centers, and even much of the research conducted within the aegis of the university is funded and driven by corporate agendas. This mode of scientific activity emphasizes applied work in settings that are directly profit-driven, featuring utility as a central norm that is operationalized through the marketability of knowledge (Jotterand, 2006). These institutional arrangements may involve fundamental shifts in important and classic values and criteria associated with science, including a loss of openness and generality, entailing related problems of credibility (Ziman, 2003).
The Laboratory and the Research Team
Regardless of the larger institutional setting, the laboratory is probably the iconic image of science. Multiple studies of the character of the laboratory have been produced in science studies (Alac, 2008; Latour & Woolgar, 1979). The character of laboratories has expanded across time (Gooday, 2008). The iconic gadget-filled den with a single eccentric investigator (and “his” sidekick) has been joined by a sprawl of open-bay style rooms under the direction of an administrator who manages a large and fully hierarchicalized crew of investigators, junior investigators, post-doctoral assistants, graduate assistants, and service personnel. Even in mid-sized laboratories, there may be as many “nonscientists” (depending on how that term is defined) involved in maintaining the facilities and workflow as there are scientific personnel who understand their actions in terms of the specific research questions that the laboratory is pursuing at any given time.
Science and Policy
The role of science in social policy (including policy about science itself) has been contested, because the goals of science (broad understanding and the open pursuit of explanation) are not well-matched to deliberation about contingent situations that demand immediate action and to decision criteria that include contested valuations and multiple, interacting, uncertain multi-level variables. Studies addressing the role science does and should play in social policies were accelerated around the turn of the 21st century because of the growth of non-academic science and the institutionalization of policy boards that gave scientists power in particular public policy areas (especially medicine and the environment), as well as due to global-level phenomena related to scientific interests (e.g., global climate change, human and animal epidemics, the spread of nuclear technologies to more nations, the decline of biodiversity, depletion of fish populations).
One of the most active strands of this research has focused on the relationship between democratic deliberation and science policy. Much recent work was instigated by government-funded initiatives that promoted and analyzed public involvement in science-related policies (Kurian & Wright, 2010). This line of research emphasizes that scientific knowledge rarely is sufficient to form sound public policy, not only because all policies require complex value judgements, but also because scientific knowledge is abstract and de-contextualized. The participation of other actors is needed to contribute the broad range of knowledge and experiences essential for the design and implementation of most public policies (Wynne, 1992). Empirical studies of public deliberation processes, however, have emphasized how difficult it is to enable idealized models of deliberation surrounding scientifically related controversies (Kerr, Cunningham-Burley, & Tutton, 2007; Kleinman, Delborne, & Anderson, 2010). The ideals themselves remain contested (Delgado, Kjølberg, & Wickson, 2011; Guston, 2014; Zorn, Roper, Weaver, & Rigby, 2012).
Discussion of Literature
Given the long history and major importance of science—however one defines science—the breadth and contested nature of the research literature about science should come as no surprise. This brief review has pointed up what many scientists and other scholars have taken to be key elements that constitute “science” while acknowledging key disputes surrounding those elements. The review has attended almost exclusively to English-based resources, and focused on the Euro-American context. Science, however, has increasingly permeated all parts of the globe. Its practice in Indian and Chinese contexts (to name only two of the largest national environments where some comparative research has been accomplished) adds additional complexity to any account of the character of science (National Science Board, 2008, figure 5.28; Holbig, 2014; Mukhopadhyay, 2014). This section provides a brief developmental perspective to highlight the relationship between changes in science and the ebb and flow of cordial and conflicting relationships between science and those who have studied the processes of science. The Web of Science lists 59 journals in the category of Philosophy and History of Science, providing a wealth of resources with different foci, depending on the specific aspect of science in which one is interested.
Historians of science have noted the impossibility of giving an exact date to science, and they have provided substantial reason to challenge the belief that science was a European rather than globally influenced development (Fara, 2009). Some components of scientific practice can be identified at least by the 8th century bce (Fara, 2009, p. 12). Reflections about what science is or ought to be have frequently been tied to the development of science itself. In arguing for the inductive and empirical approach to knowledge generation, Sir Francis Bacon (1855) provided substantial rationales contributing to the identity of science, and therefore might be identified as a “first” theorist of science. However, the term “scientist” did not come into circulation until 1833 (Fara, 2009, p. 191). Important developments in mathematics and philosophy (such as David Hume’s analysis of induction) can be seen as contributing simultaneously to the formation of science and to “science studies”—that is, the study of science.
Although it would not be possible to identify all of the key trends and turning points in this highly multidisciplinary area, there are some markers of 20th-century science studies that have had multidisciplinary impact. Isis (a journal dedicated to the history of science) was inaugurated in 1912, and the History of Science Society was founded in 1924. In philosophy of science, Karl Popper’s Logic of Scientific Discovery (published in German in 1934 and translated to English in 1959) offered an account of science based not in the positive accumulation of knowledge through rigorous experimentation and observation, but rather a negative account of the method as capable solely of dis-validating hypotheses. This attempt to formulate a deductive basis of science—and discussion of its limits by many critics (Feyeraband, 2010; Polanyi, 1958), but especially Thomas Kuhn’s (1962) The Structure of Scientific Revolutions, gained substantial multidisciplinary attention to the central issues regarding the underlying principles and epistemic status of science.
As the scientific method and its assumptions gained widespread recognition in the academy, empirical studies of science itself were undertaken not only by historians but also by sociologists. Such studies could be undertaken in a descriptive mode that accepted scientific conclusions or, especially after Bloor (1991; Bloor, Barnes, & Henry, 1996) inaugurated the so-called “strong programme” of science studies, they could proceed with the assumption that there was no epistemic authority to science-based conclusions. As the products of scientific research came to be seen as increasingly powerful and problematic in the 1970s and 1980s—with the contexts of the atom bomb, atomic energy, use of evolutionary theories to support gender and racial discrimination, and ecological challenges—the influence of those critical of science grew in the academy (Farrell & Goodnight, 1981; Fox Keller & Longino, 1996; Oravec, 1982). Simultaneously, conflicts over what counted as science raised the question of how science was “demarcated” from nonscience (Gieryn, 1999; Taylor, 1996).
The debate between science and its critics came to be dubbed the “science wars,” which arguably culminated in the “Sokal Hoax” in 1996. Physics professor Alan Sokal (1996) submitted a faux paper to the journal Social Text, which accepted it and published it in a special issue on the “Science Wars.” Defenders of science interpreted the publication of the faux postmodernist essay as a demonstration of the vacuity of the social constructionist’s critique of science. The ideologically and epistemically hostile critics of science offered a variety of replies (Ross, 1997 and other authors in that issue).
The overlaps in the “strong programme,” “poststructuralist,” “social constructionist,” and various ideologically grounded critiques of science had been diffusely imbibed but not univocally absorbed by scholars in philosophy of science and history of science. Even in rhetorical and communication studies of science—which were predisposed by disciplinary logics toward constructivist assumptions—a wealth of lines of analysis developed as the end of the century approached. Perhaps the earliest article in the contemporary “rhetoric of science” stream was John Campbell’s (1970) analysis of the rhetoric employed by Charles Darwin. By 1980, the interdisciplinary Project on the Rhetoric of Inquiry (“POROI”) had been founded at the University of Iowa. In the late 1980s, Gross (1990), Lyne and Howe (1986), Myers (1990), and Prelli (1989), among others, had begun to explore both detailed cases of the rhetoric employed by scientists and trends in scientific rhetoric across particular cases. The Association for the Rhetoric of Science and Technology was founded in 1992, supporting a particularly strong emphasis on the fast-growing fields of biology (Ceccarelli, 2001; Condit, 1999; Taylor, 1996).
This acceleration of growth continued in the 21st century in both communication studies and English, in part both stimulated and challenged by two social factors. First, a growing societal emphasis on health and medicine was amplified by the funding interests of universities to stimulate the flourishing of studies of rhetoric in biomedical fields (Lynch, 2011; Keränen, 2010; Koerber, 2013; Segal, 2005). Simultaneously, the mass marketing successes of political resistance to scientific claims regarding climate change and other environmental impacts of industry, Darwinian evolution, and health programs such as HIV prevention and contraception produced a shift in the balance of concerns among at least some rhetoricians studying science, augmenting the theoretically totalistic strands with more complex or multiple positionings of rhetorical analyses in relationship to science (Ceccarelli, 2011; Condit & Lynch, 2012; Gross, 2011; Koerber, 2013; Lynch, 2009; Paroske, 2009). A recent increase in the rhetoric of economics (previously inaugurated by McCloskey, 1985) promises to add to the reach of these studies.
Alongside rhetorical analyses, the interdisciplinary area of science communication continued robust publication of critical analyses of particular episodes of scientific discourse (Nerlich & Hellsten, 2004), studies of public discourse about science (Schäfer, 2012; Strekalova, 2015), and explorations of public understandings of science (Hanson-Easey, Williams, Hansen, Fogarty, & Bi, 2015; Jee, Uttal, Spiegel, & Diamond, 2015). Pragmatic concerns with science education (Hobbs, 2015) and effective communication of science (Ceccarelli, 2011; Gronnvoll & Landau, 2010; Lipkus, 2007; Nerlich, Koteyko, & Brown, 2009; Park, 2001; Spoel, Goforth, Cheu, &Pearson, 2009) also featured in this area. The enormous research area of health communication overlapped with science studies.
In sum, the character of science has changed through time, and these changes have included different imbrications in the broader society. These changes have inevitably been reflected in both academic and public understandings of what science itself is or does.
Primary Sources and Further Reading
The range of science and science studies is too enormous to allow even a concise treatment of primary sources. A useful starting point is to seek out key organizations dedicated to science broadly understood (American Association for the Advancement of Science, China Association for Science and Technology, Indian Science Congress Association, The Royal Society, The Science Council of Japan, or organizations specific to a discipline, which may be nationally or internationally organized, such as the National Communication Association or the International Communication Association). Organizations dedicated to the study of science as a process or entity include the History of Science Society, the Society for the History of Technology, the Society for the Social Studies of Science, the Philosophy of Science Association, and the Association for the Rhetoric of Science and Technology.
A rich history of science is provided by Fara (2009). Bubela et al. (2009) provide a primer for communicating about science with the public that is reflective of the early-21st-century perspectives. Fuller (1988) provides one influential perspective in science studies. Useful beginning points for surveying science from the perspective of the scholar of communication include Fahnestock (1999), Harris (1997), Gross (2011), and Bazerman (1989). Placement of issues relevant to communication studies in the broader arena of science studies can be gained by consulting Jasanoff, Markel, Petersen, and Pinch(1995) and Fuller and Collier (2004). Classic feminist studies of relevance include Harding (1986) and Martin (1991). Recent deep engagements with the issue of expertise are represented by Collins (2007).
Ackerman, R. (1970). Philosophy of science: An introduction. New York: Pegasus.Find this resource:
Ahn, A. C., Tewari, M., Poon, C. S., & Phillips, R. S. (2006). The limits of reductionism in medicine: Could systems biology offer an alternative?PLoS Medicine, 3(6), e208.Find this resource:
Alac, M. (2008). Working with brain scans: Digital images and gestural interaction in fMRI laboratory. Social Studies of Science, 38(4), 483–508.Find this resource:
Allen, J. F. (2001). Bioinformatics and discovery: Induction beckons again. BioEssays, 23, 104–107.Find this resource:
American Association for the Advancement of Science. (1989). Chapter 1: The nature of science. In Science for all Americans: Educating for a changing future. Washington, DC: American Association for the Advancement of Science.Find this resource:
Anderson, M. S., & Louis, K. S. (1994). The graduate student experience and subscription to the norms of science. Research in Higher Education, 35(3), 273–299.Find this resource:
Anderson, M. S., Ronning, E. A., DeVries, R., & Martinson, B. C. (2010). Extending the Mertonian norms: Scientists’ subscription to norms of research. Journal of Higher Education, 81(3), 366–393.Find this resource:
Bacon, F. (1855). The novum organon. Oxford, U.K.: Oxford University Press. Originally published in 1620.Find this resource:
Bagla, P. (2012). Crowd-sourcing drug discovery. Science, 335 (6071), 909.Find this resource:
Bailer-Jones, D. M. (2009). Scientific models in philosophy of science. Pittsburgh: University of Pittsburgh Press.Find this resource:
Bauer, H. H. (1992). Scientific literacy and the myth of the scientific method. Urbana: University of Illinois Press.Find this resource:
Bazerman, C. (1989). Shaping written knowledge: The genre and activity of the experimental article in science. Madison: University of Wisconsin Press.Find this resource:
Bazerman, C. (1997). Reporting the experiment: The changing account of scientific doings in the philosophical transactions of the Royal Society, 1665–1800. In R. A. Harris (Ed.), Landmark essays on rhetoric of science: Case studies (pp. 169–186). Mahwah, NJ: Hermagoras Press.Find this resource:
Bloor, D. (1991). Knowledge and social imagery (2d ed.). Chicago: University of Chicago Press.Find this resource:
Bloor, D., Barnes, B., & Henry, J. (1996). Scientific knowledge: A sociological analysis. Chicago: University of Chicago Press.Find this resource:
Bubela, T., Nisbet, M. C., Borchelt, R., Brunger, F., Critchley, C., Einsiedel, E., et al. (2009). Science communication reconsidered. Nature Biotechnology, 27(6), 514–518.Find this resource:
Campbell, J. A. (1970). Darwin and the origin of species. Speech Monographs, 27, 1–14.Find this resource:
Ceccarelli, L. (2001). Shaping science with rhetoric: The cases of Dobzhansky, Schrodinger, and Wilson. Chicago: University of Chicago Press.Find this resource:
Ceccarelli, L. (2011). Manufactured scientific controversy: Science, rhetoric, and public debate. Rhetoric & Public Affairs, 14(2), 195–228.Find this resource:
Chalmers, A. (2013). What is this thing called science? New and Extended Edition. Brisbane, AU: University of Queensland Press.Find this resource:
Collins, F. (2014, May 6). Mining the big data mountain. NIH director’s blog.Find this resource:
Collins, H. M. (2007). Rethinking expertise. Chicago: University of Chicago Press.Find this resource:
Condit, C. (1996). How bad science stays that way: Of brain sex, demarcation, and the status of truth in the rhetoric of science. Rhetoric Society Quarterly, 26, 83–109.Find this resource:
Condit, C. M. (1999). The meanings of the gene: Public debates about human heredity. Madison: University of Wisconsin Press.Find this resource:
Condit, C. M., & Lynch, J. (2012). Recent rhetorical studies in public understanding of science: Multiple purposes and strengths. Public Understanding of Science, 21(4), 386–400.Find this resource:
Culshaw, R. V. (2006). Mathematical modeling of AIDS progression: Limitations, expectations, and future directions. Journal of American Physicians and Surgeons, 11(4), 101–105.Find this resource:
Debru, C. (2013). Claude Bernard: La médicine expérimentale. Méthode scientifique/Pratiques scientifiques et épistémologie.Find this resource:
Delgado, A., Kjølberg, K. L., & Wickson, F. (2011). Public engagement coming of age: From theory to practice in STS encounters with nanotechnology. Public Understanding of Science, 20(6), 826–845.Find this resource:
Depew, D., & Lyne, J. (2013). The productivity of scientific rhetoric. Poroi, 9(1), article 4.Find this resource:
Fahnestock, J. (1999). Rhetorical figures in science. New York: Oxford University Press.Find this resource:
Fara, P. (2009). Science: A fourth thousand year history. Oxford, U.K.: Oxford University Press.Find this resource:
Farrell, T. B., & Goodnight, T. (1981). Accidental rhetoric: The root metaphors of Three Mile Island. Communication Monographs, 48, 271–300.Find this resource:
Feyerabend, P. (2010). Against method: Outline of an anarchistic theory of knowledge (4th ed.). London: Verso.Find this resource:
Fox Keller, E., & Longino, H. E. (Eds.). (1996). Feminism and science. Oxford, U.K.: Oxford University Press.Find this resource:
Franklin, J. (2009). What science knows and how it knows it. New York: Encounter Books.Find this resource:
Fuccillo K. K., Crimmins, T. M., de Rivera, C. E., & Elder, T. S. (2014). Assessing accuracy in citizen science–based plant phenology monitoring. International Journal of Biometeorology, 59(7), 917–926.Find this resource:
Fuller, S. (1988). Social epistemology. Bloomington: Indiana University Press.Find this resource:
Fuller, S., & Collier, J. H. (2004). Philosophy, rhetoric, and the end of knowledge: A new beginning for science and technology studies (2d ed.). Mahwah, NJ: Lawrence Erlbaum.Find this resource:
Gallagher, R., & Appenzeller, T. (1999). Beyond reductionism. Science, 284 (5411), 79–109.Find this resource:
Gieryn, T. (1999). Cultural boundaries of science: Credibility on the line. Chicago: University of Chicago Press.Find this resource:
Gooday, G. (2008). Placing or replacing the laboratory in the history of science. Isis, 99, 783–795.Find this resource:
Gronnvoll, M., & Landau, J. (2010). From viruses to Russian roulette to dance: A rhetorical critique and creation of genetic metaphors. Rhetoric Society Quarterly, 40(1), 46–70.Find this resource:
Gross, A. (1990). The rhetoric of science. Cambridge, MA: Harvard University Press.Find this resource:
Gross, A. (2011). Starring the text: The place of rhetoric in science studies. Carbondale: Southern Illinois University Press.Find this resource:
Guston, D. H. (2014). Understanding “anticipatory governance”. Social Studies of Science, 44, 218–242.Find this resource:
Hanson-Easey, S., Williams, S., Hansen, S., Fogarty, K., & Bi, P. (2015). Speaking of climate change: A discursive analysis of lay understandings. Science Communication, 37, 217–239.Find this resource:
Harding, S. (1986). The science question in feminism. Ithaca, NY: Cornell University Press.Find this resource:
Harris, R. A. (Ed.). (1997). Landmark essays on rhetoric of science: Case studies. Mahwah, NJ: Lawrence Erlbaum.Find this resource:
Hellström, T., Jacob, M., & Wenneberg, S. B. (2003). The “discipline” of post-academic science: Reconstructing the paradigmatic foundations of a virtual research institute. Science and Public Policy, 30(4), 251–260.Find this resource:
Hobbs, L. K. (2015). Play-based science learning activities: Engaging adults and children with informal science learning for preschoolers. Science Communication, 37, 405–414.Find this resource:
Holbig, H. (2014). Shifting ideologics of research funding: The CPC’s National Planning Office for Philosophy and Social Sciences. Journal of Current Chinese Affairs, 43(2), 13–32.Find this resource:
Hong, S. (2004). Building circuits of trust. Science, 305, 1569–1570.Find this resource:
Ioannidis, J. P. A. (2005). Why most published research findings are false. PLoS Medicine, 2(8), e124.Find this resource:
Jasanoff, S., Markel, G., Petersen, J., & Pinch, T. (Eds.). (1995). Handbook of science and technology studies. Thousand Oaks, CA: SAGE.Find this resource:
Jee, B. D., Uttal, D. H., Spiegel, A., & Diamond, J. (2015). Expert-novice differences in mental models of viruses, vaccines, and the causes of infectious disease. Public Understanding of Science, 24, 241–256.Find this resource:
Jerit, J., Barabas, J., & Clifford, S. (2013). Comparing contemporaneous laboratory and field experiments on media effects. Public Opinion Quarterly, 77(1), 256–282.Find this resource:
Jotterand, F. (2006). The politicization of science and technology: Its implication for nanotechnology. Journal of Law, Medicine & Ethics, 34, 658–666.Find this resource:
Keppel, G. (1991). Design and analysis: A researcher’s handbook (3d ed.). Upper Saddle River, NJ: Prentice Hall.Find this resource:
Keränen, L. (2010). Scientific characters: Rhetoric, politics, and trust in breast cancer research. Tuscaloosa: University of Alabama Press.Find this resource:
Kerr, A., Cunningham-Burley, S., & Tutton, R. (2007). Shifting subject positions: Experts and lay people in public dialogue. Social Studies of Science, 37(3), 384–411.Find this resource:
Kleinman, D. L., Delborne, J. A., & Anderson, A. A. (2010). Engaging citizens: The high cost of citizen participation in high technology. Public Understanding of Science, 20(2), 221–240.Find this resource:
Koerber, A. (2013). Breast or bottle: Contemporary controversies in infant-feeding policy and practice. Columbia: University of South Carolina Press.Find this resource:
Krippendorff, K. (2004). Content analysis: An introduction to its methodology (2d ed.). Thousand Oaks, CA: SAGE.Find this resource:
Kuhn, T. S. (1962). The structure of scientific revolutions. Chicago: University of Chicago Press.Find this resource:
Kurian, P., & Wright, J. (2010). Science, governance, and public participation: An analysis of decision making on genetic modification in Aotearoa/New Zealand. Public Understanding of Science, 21(4), 447–464.Find this resource:
Larivière, V., Haustein, S., & Mongeon, P. (2015). The oligopoly of academic publishers in the digital era. PLoS One, 10(6), e0127502.Find this resource:
Latour, B., & Woolgar, S. (1979). Laboratory life: The social construction of scientific facts. Beverly Hills, CA: SAGE.Find this resource:
Ledford, H. (2012). Sponsor my science. Nature, 481, 254–255.Find this resource:
Lipkus, I. M. (2007). Numeric, verbal, and visual formats of conveying health risks: Suggested best practices and future recommendations. Medical Decision Making, 27, 696–713.Find this resource:
Lynch, J. A. (2009). Articulating scientific practice: Understanding Dean Hamer’s “Gay Gene” study as overlapping material, social, and rhetorical registers. Quarterly Journal of Speech, 95(4), 435–456.Find this resource:
Lynch, J. (2011). What are stem cells? Definitions at the intersection of science and politics. Tuscaloosa: University of Alabama Press.Find this resource:
Lyne, J., & Howe, H. F. (1986). “Punctuated equilibria”: Rhetorical dynamics of a scientific controversy. Quarterly Journal of Speech, 72(2), 132–147.Find this resource:
Martin, E. (1991). The egg and the sperm: How science has constructed a romance based on stereotypical male-female roles. Signs, 16(3), 485–501.Find this resource:
McCloskey, D. N. (1985). The rhetoric of economics. Madison: University of Wisconsin Press.Find this resource:
Merton, R. K. (1973). The normative structure of science. In R. K. Merton (Ed.), The Sociology of Science: Theoretical and Empirical Investigations (pp. 267–280). Chicago: University of Chicago Press.Find this resource:
Mitroff, I. I. (1974). Norms and counter-norms in a select group of the Apollo moon scientists: A case study of the ambivalence of scientists. American Sociological Review, 39(4), 579–595.Find this resource:
Mukhopadhyay, D. (2014). Post-independence science policy and science funding in India. Current Science, 107(12), 1983–1987.Find this resource:
Mulkay, M. (1980). Interpretation and the use of rules: The case of the rules of science. Transactions of the New York Academy of Sciences, 39(1) (Series II), 111–124.Find this resource:
Myers, G. (1990). Writing biology: Texts and the social construction of scientific knowledge. Madison: University of Wisconsin Press,Find this resource:
National Science Board. (2008). Science and engineering indicators 2008. Arlington, VA: National Science Foundation.Find this resource:
Nerlich, B., & Hellsten, I. (2004). Genomics: Shifts in metaphorical landscape between 2000 and 2003. New Genetics and Society, 23(3), 255–268.Find this resource:
Nerlich, B., Koteyko, N., & Brown, B. (2009). Theory and language of climate change communication. Wiley Interdisciplinary Reviews: Climate Change, 1(1), 97–110.Find this resource:
Nov, O., Arazy, O., & Anderson, D. (2014). Scientists@Home: What drives the quantity and quality of online citizen science participation?PLoS ONE, 9(4), e90375.Find this resource:
O’Keefe, D. J. (1975). Logical empiricism and the study of human communication. Speech Monographs, 42(3), 171–183.Find this resource:
Open Science Collaboration. (2015). Estimating the reproducibility of psychological science. Science, 349(6251).Find this resource:
Oravec, C. (1982). The evolutionary sublime and the essay of natural history. Communication Monographs, 49(4), 215–229.Find this resource:
Oreskes, N., Shrader-Frechette, K., & Belitz, K. (1994). Verification, validation, and confirmation of numerical models in the earth sciences. Science, 263(5147), 641–646.Find this resource:
Park, H. J. (2001). The creation-evolution debate: Carving creationism in the public mind. Public Understanding of Science, 10(2), 173–186.Find this resource:
Paroske, M. (2009). Deliberating international science policy controversies: Uncertainty and AIDS in South Africa. Quarterly Journal of Speech, 95(2), 148–170.Find this resource:
Pickering, A. (1995). The mangle of practice: Time, agency, and science. Chicago: University of Chicago Press.Find this resource:
Polanyi, M. (1958). Personal knowledge: Towards a post-critical philosophy. Chicago: University of Chicago Press.Find this resource:
Popper, K. (1959). The logic of scientific discovery. New York: Harper and Row.Find this resource:
Porter, T. M. (1995). Trust in numbers: The pursuit of objectivity in science and public life. Princeton, NJ: Princeton University Press.Find this resource:
Prelli, L. (1989). A rhetoric of science: Inventing scientific discourse. Columbia: University of South Carolina Press.Find this resource:
Quarteroni, A. (2009). Mathematical models in science and engineering. Notices of the AMS, 56(1), 10–19.Find this resource:
de Ridder-Symoens, H. (Ed.). (1996). A history of the university in Europe. Vol. 2. Universities in early modern Europe, 1500–1800. New York: Cambridge University Press. (See especially p. 38.)Find this resource:
Rodriguez, V. (2007). Merton and Ziman’s modes of science: The case of biological and similar material transfer agreements. Science and Public Policy, 34(5), 355–363.Find this resource:
Ross, A. (1997). Reflections on the Sokal Affair. Social Text, 50, 149–152.Find this resource:
Sarasohn, L. T. (1993). Nicolas-Claude Fabri de Peiresc and the patronage of the New Science in the Seventeenth Century. Isis, 84, 70–90.Find this resource:
Schäfer, M. S. (2012). Taking stock: A meta-analysis of studies of the media’s coverage of science. Public Understanding of Science, 21(6), 650–663.Find this resource:
Schiebinger, L. (2000). Has feminism changed science? Signs, 25(4), 1171–1175.Find this resource:
Science Council (United Kingdom) (n.d.). Our definition of science.Find this resource:
Segal, J. Z. (2005). Health and the rhetoric of medicine. Carbondale: Southern Illinois University Press.Find this resource:
Snow, C. P. (1959). The two cultures and the scientific revolution. New York: Cambridge University Press.Find this resource:
Sokal, A. D. (1996). Transgressing the boundaries: Toward a transformative hermeneutics of quantum gravity. Social Text, 46–47, 217–252.Find this resource:
Spoel, P., Goforth, D., Cheu, H., & Pearson, D. (2009). Public communication of climate change science: Engaging citizens through apocalyptic narrative explanation. Technical Communication Quarterly, 18(1), 49–81.Find this resource:
Strekalova, Y. A. (2015). Informing dissemination research: A content analysis of U.S. newspaper coverage of medical nanotechnology news. Science Communication, 37, 151–172.Find this resource:
Taylor, C. A. (1996). Defining science: A rhetoric of demarcation. Madison: University of Wisconsin Press.Find this resource:
Toulmin, S. (1958). The uses of argument. Cambridge, U.K.: Cambridge University Press.Find this resource:
Whithaus, C. (2012). Claim-evidence structures in environmental science writing: Modifying Toulmin’s model to account for multimodal arguments. Technical Communication Quarterly, 21(2), 105–128.Find this resource:
Wynn, J. (2009). Arithmetic of the species: Darwin and the role of mathematics in his argumentation. Rhetorica, 27(2), 76–97.Find this resource:
Wynne, B. (1992). Misunderstood misunderstandings: Social identities and public uptake of science. Public Understanding of Science, 1, 281–304.Find this resource:
Ziman, J. (1978). Reliable knowledge: An exploration of the grounds for belief in science. Cambridge, U.K.: Cambridge University Press.Find this resource:
Ziman, J. (1993). “Postacademic Science”: Constructing knowledge with networks and norms. Science Studies, 9(1), 67–70.Find this resource:
Ziman, J. (2003). Non-instrumental roles of science. Science and Engineering Ethics, 9, 17–27.Find this resource:
Zorn, T. E., Roper, J., Weaver, C. K., & Rigby, C. (2012). Influence in science dialogue: Individual attitude changes as a result of dialogue between laypersons and scientists. Public Understanding of Science, 21(7), 848–864.Find this resource: