Scientific theories must meet the following criteria are supported

scientific theories must meet the following criteria are supported

A scientific theory is an explanation of an aspect of the natural world that can be repeatedly The essential criterion is that the theory must be observable and repeatable. In addition, scientists prefer to work with a theory that meets the following However, theories supported by the scientific consensus have the highest. The theory should have substantial design application but can deal with any topic . . of diverse natures—sometimes designers follow a systematic and structured Research data is gathered primarily to support the creation of a satisfactory. Scientific theories are like philosophies: they are sets of beliefs. . to find data that is compatible with a given theory. The explanandum should follow from the .

The dominant perspective on correspondence rules is that they interpret theoretical terms. Instead, scientific theories require observational interpretation through correspondence rules. Even so, surplus meaning always remains in the theoretical structure Hempel87; Carnap Second, correspondence rules are seen as necessary for inter-theoretic reduction van Riel and Van Gulick Notably, NagelChapter 11; and Schaffner, allow for multiple kinds of correspondence rules, between terms of either vocabulary, in the reducing and the reduced theory cf.

Correspondence rules are a core part of the structure of scientific theories and serve as glue between theory and observation. Finally, while they are not part of the theory structure, and although we saw some examples above, observation sentences are worth briefly reviewing. Correspondence rules attach to the content of observational sentences. Although constrained by Calc, the grammar of these sentences is determined primarily by the order of nature, as it were.

In general, syntacticists do not consider methods of data acquisition, experiment, and measurement to be philosophically interesting.

In contrast, the confirmation relation between collected data and theory, especially as developed in inductive logic e. Syntactic View To summarize, the Syntactic View holds that there are three kinds of terms or vocabularies: Moreover, the structure of scientific theories could be analyzed using the logical tools of metamathematics. The goal is to reconstruct the logic of science, viz. Furthermore, for purposes of the syntactic reconstruction of scientific theories, some continue espousing—or perhaps plea for the resurrection of—predicate logic e.

The Semantic View An overarching theme of the Semantic View is that analyzing theory structure requires employing mathematical tools rather than predicate logic. After all, defining scientific concepts within a specific formal language makes any axiomatizing effort dependent on the choice, nature, and idiosyncrasies of that narrowly-defined language. Indeed, what would the appropriate logical language for specific mathematical structures be, especially when such structures could be reconstructed in a variety of formal languages?

Why should we imprison mathematics and mathematical scientific theory in syntactically defined language s when we could, instead, directly investigate the mathematical objects, relations, and functions of scientific theory?

Consistent with the combat strategy discussed in the Conclusionhere is a list of grievances against the Syntactic View discussed at length in the work of some semanticists. First-Order Predicate Logic Objection. This places heavy explanatory and representational responsibility on relatively inflexible and limited languages. Since theories are individuated by their linguistic formulations, every change in high-level syntactic formulations will bring forth a distinct theory.

This produces a reductio: There is no clear way of distinguishing between intended and unintended models for syntactically characterized theories e. Confused Correspondence Rules Objection. Correspondence rules are a confused medley of direct meaning relationships between terms and world, means of inter-theoretic reduction, causal relationship claims, and manners of theoretical concept testing.

Trivially True yet Non-Useful Objection. Presenting scientific theory in a limited axiomatic system, while clearly syntactically correct, is neither useful nor honest, since scientific theories are mathematical structures. Practice and History Ignored Objection. Syntactic approaches do not pay sufficient attention to the actual practice and history of scientific theorizing and experimenting. Even a minimal description of the Semantic View must acknowledge two distinct strategies of characterizing and comprehending theory structure: An actual, real system can take on, and change, states according to different kinds of laws, viz.

Different models of a given theory will share some dimensions of their state space while differing in others. Such models will also partially overlap in laws for further discussion of state spaces, laws, and models pertinent to the Semantic View, see Suppe—8; LloydChapter 2; Nolte ; Weisberg26—9.

Suppe ; van Fraassen65—67; Lorenzano ; advocates of the approach include: Beatty ; Giere; Giere, Bickle, and Mauldin ; Lloyd[], In Press; Suppe; Thompson,; van Fraassen, ; for alternative early analyses of models see, e.

A more fine-grained classification of the state-space approach is desirable, particularly if we wish to understand important lessons stemming from the Pragmatic View of Theories, as we shall see below.

As an example of a state-space analysis of modeling, consider a capsule traveling in outer space. If the mass were unknown or permitted to vary, we would have to add one more dimension. Possible and actual trajectories of our capsule, with known mass, within this abstract 9-dimensional state space could be inferred via Newtonian dynamical laws of motion example in Lewontin6—8; consult Suppe4.

Importantly, under the state-space approach, the interesting philosophical work of characterizing theory structure e. Set theory is a general language for formalizing mathematical structures as collections—i.

Interestingly, model theory often uses set theory e. An example will help motivate the relation between theory and model. Two qualifications are required: In topology and geometry there is rich background theory regarding how to close Euclidean planes and spaces to make finite geometries by, for instance, eliminating parallel lines.

Consider the axioms of a projective plane: For any two points, exactly one line lies on both. For any two lines, exactly one point lies on both. There exists a set of four points such that no line has more than two of them.

A figure of a geometric model that makes this theory true is: This is the smallest geometrical model satisfying the three axioms of the projective plane theory. A model is called a model of a theory exactly if the theory is entirely true if considered with respect to this model alone.

Of course, our universe is bigger. Because Euclidean geometry includes parallel lines, the Fano plane is not a model of Euclidean geometry. Even so, by drawing the plane, we have shown it to be isomorphic to parts of the Euclidean plane.

scientific theories must meet the following criteria are supported

In other words, the Fano plane has been embedded in a Euclidean plane. Below we return to the concepts of embedding and isomorphism, but this example shall suffice for now to indicate how a geometric model can provide a semantics for the axioms of a theory. In short, for the Semantic View the structure of a scientific theory is its class of mathematical models.

Theory and Observation in Science

According to some advocates of this view, the family of models can itself be axiomatized, with those very models or other models serving as axiom truth-makers.

Suppes and his collaborators defined seven axioms—three kinematical and four dynamical—characterizing Newtonian particle mechanics see also Simon Once these axioms are made explicit, their models can be specified and these can, in turn, be applied to actual systems, thereby providing a semantics for the axioms e. A particular system satisfying these seven axioms is a particle mechanics system. For an example of Newtonian mechanics from the state-space approach, recall the space capsule of Section 3.

How do we connect theory and data via observation and experimental and measuring techniques? The Semantic View distinguishes theory individuation from both theory-phenomena and theory-world relations.

Three types of analysis of theory interpretation are worth investigating: These models include models of theory, models of experiment, and models of data Suppes Here is a summary of important parts of the hierarchy SuppesTable 1, ; cf. GiereFigure 1, Axioms define set-theoretic predicates, and constitute the core structure of scientific theories, as reviewed in Section 3.

Representation theorem methodology can be extended i down the hierarchy, both to models of experiment and models of data, and ii from isomorphism to homomorphism Suppesp. Criteria of experimental design motivate choices for how to set up and analyze experiments.

There are complex mappings between models of experiment thus specified, and i models of theory, ii theories of measurement, and iii models of data. In building models of data, phenomena are organized with respect to statistical goodness-of-fit tests and parameter estimation, in the context of models of theory.

Choices about which parameters to represent must be made. The temptation to place phenomena at the bottom of the hierarchy must be resisted because phenomena permeate all levels. Furthermore, this temptation raises fundamental questions about scientific representation: According to Giere, interpretation is mediated by theoretical hypotheses positing representational relations between a model and relevant parts of the world. Such relations may be stated as follows: Model-world similarity judgments are conventional and intentional: Note that I am not saying that the model itself represents an aspect of the world because it is similar to that aspect.

It is not the model that is doing the representing; it is the scientist using the model who is doing the representing. Giere and Weisberg thus endorse an abundance of adequate mapping relations between a given model and the world.

From this diversity, scientists and scientific communities must select particularly useful similarity relationships for contextual modeling purposes. Because of semantic pluralism and irreducible intentionality, this similarity analysis of theory interpretation cannot be accommodated within a hierarchy of models approach, interpreted as a neat model nesting based on pre-given semantic relations among models at different levels. Figure 2 literally and figuratively captures the term: Figure 3 indicates five different kinds of homomorphism, arranged in a Venn diagram.

Although philosophers have focused on isomorphism, other morphisms such as monomorphism i. To complete the visualization above, an epimorphism is a surjective homomorphism, and an endomorphism is a mapping from a structure to itself, although it need not be a symmetrical—i.

Perhaps the most avid supporter of isomorphism and embedding as the way to understand theory interpretation is van Fraassen. Speaking metaphorically, the phenomena are, from a theoretical point of view, small, arbitrary, and chaotic—even nasty, brutish, and short…—but can be understood as embeddable in beautifully simple but much larger mathematical models.

Bueno, da Costa, French, and Ladyman also employ embedding and partial isomorphism in the empirical interpretation of partial structures Bueno ; Bueno, French, and Ladyman ; da Costa and French; French and Ladyman, ; Ladyman On the one hand, representation is structural identity between the theoretical and the empirical. Instead, commitments to the former are logically and actually separable from positions on the latter e. The Pragmatic View The Pragmatic View recognizes that a number of assumptions about scientific theory seem to be shared by the Syntactic and Semantic Views.

Both perspectives agree, very roughly, that theory is 1 explicit, 2 mathematical, 3 abstract, 4 systematic, 5 readily individualizable, 6 distinct from data and experiment, and 7 highly explanatory and predictive see Flyvbjerg38—39; cf.

The Pragmatic View imagines the structure of scientific theories rather differently, arguing for a variety of theses: Idealized theory structure might be too weak to ground the predictive and explanatory work syntacticists and semanticists expect of it e.

Theory structure is plural and complex both in the sense of internal variegation and of existing in many types. In other words, there is an internal pluralism of theory and model components e.

Indeed, it may be better to speak of the structures of scientific theories, in the double-plural. The internal pluralism of theory structure thesis 2 includes many nonformal aspects deserving attention. Characterizations of the nature and dynamics of theory structure should pay attention to the user as well as to purposes and values e.

These are core commitments of the Pragmatic View. It is important to note at the outset that the Pragmatic View takes its name from the linguistic trichotomy discussed above, in the Introduction.

This perspective need not imply commitment to, or association with, American Pragmatism e. Peirce, William James, or John Dewey; cf.

Hookway ; Richardson For instance, Hacking a distinguishes his pragmatic attitudes from the school of Pragmatism. The Galilean quantities would be of no interest to an Aristotelian who treats the stone as falling under constraint toward the center of the earth Kuhn Thus Galileo and the Aristotelian would not have collected the same data. Absent records of Aristotelian pendulum experiments we can think of this as a thought experiment.

Salience and theoretical stance Taking K1, K2, and K3 in order of plausibility, K3 points to an important fact about scientific practice. Sometimes these include theoretical commitments that lead experimentalists to produce non-illuminating or misleading evidence.

In other cases they may lead experimentalists to ignore, or even fail to produce useful evidence. For example, in order to obtain data on orgasms in female stumptail macaques, one researcher wired up females to produce radio records of orgasmic muscle contractions, heart rate increases, etc. Although female stumptail orgasms occuring during sex with males are atypical, the experimental design was driven by the assumption that what makes features of female sexuality worth studying is their contribution to reproduction Lloyd When they do, investigators are often able eventually to make corrections, and come to appreciate the significance of data that had not originally been salient to them.

Thus paradigms and theoretical commitments actually do influence saliency, but their influence is neither inevitable nor irremediable. They often draw, photograph, make audio recordings, etc. But disagreements about the epistemic import of a graph, picture or other non-sentential bit of data often turn on causal rather than semantical considerations. Anatomists may have to decide whether a dark spot in a micrograph was caused by a staining artifact or by light reflected from an anatomically significant structure.

Physicists may wonder whether a blip in a Geiger counter record reflects the causal influence of the radiation they wanted to monitor, or a surge in ambient radiation. Chemists may worry about the purity of samples used to obtain data.

Such questions are not, and are not well represented as, semantic questions to which K2 is relevant. Late 20th century philosophers may have ignored such cases and exaggerated the influence of semantic theory loading because they thought of theory testing in terms of inferential relations between observation and theoretical sentences.

With regard to sentential observation reports, the significance of semantic theory loading is less ubiquitous than one might expect. The interpretation of verbal reports often depends on ideas about causal structure rather than the meanings of signs.

Rather than worrying about the meaning of words used to describe their observations, scientists are more likely to wonder whether the observers made up or withheld information, whether one or more details were artifacts of observation conditions, whether the specimens were atypical, and so on.

Kuhnian paradigms are heterogeneous collections of experimental practices, theoretical principles, problems selected for investigation, approaches to their solution, etc. Connections between components are loose enough to allow investigators who disagree profoundly over one or more theoretical claims to agree about how to design, execute, and record the results of their experiments.

Looking at a patient with red spots and a fever, an investigator might report having seen the spots, or measles symptoms, or a patient with measles. Watching an unknown liquid dripping into a litmus solution an observer might report seeing a change in color, a liquid with a PH of less than 7, or an acid.

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The appropriateness of a description of a test outcome depends on how the relevant concepts are operationalized. What justifies an observer to report having observed a case of measles according to one operationalization might require her to say no more than that she had observed measles symptoms, or just red spots according to another.

Theory and Observation in Science (Stanford Encyclopedia of Philosophy)

Bridgman5 one might suppose that operationalizations are definitions or meaning rules such that it is analytically true, e. But it is more faithful to actual scientific practice to think of operationalizations as defeasible rules for the application of a concept such that both the rules and their applications are subject to revision on the basis of new empirical or theoretical developments.

So understood, to operationalize is to adopt verbal and related practices for the purpose of enabling scientists to do their work. Operationalizations are thus sensitive and subject to change on the basis of findings that influence their usefulness Feest, Definitional or not, investigators in different research traditions may be trained to report their observations in conformity with conflicting operationalizations. Thus instead of training observers to describe what they see in a bubble chamber as a whitish streak or a trail, one might train them to say they see a particle track or even a particle.

This may reflect what Kuhn meant by suggesting that some observers might be justified or even required to describe themselves as having seen oxygen, transparent and colorless though it is, or atoms, invisible though they are. Kuhnff To the contrary, one might object that what one sees should not be confused with what one is trained to say when one sees it, and therefore that talking about seeing a colorless gas or an invisible particle may be nothing more than a picturesque way of talking about what certain operationalizations entitle observers to say.

Some would expect enough agreement to secure the objectivity of observational data. Still others would try to supply different standards for objectivity. Is perception theory laden? Furthermore proponents of incompatible theories often produce impressively similar observational data. Much as they disagreed about the nature of respiration and combustion, Priestley and Lavoisier gave quantitatively similar reports of how long their mice stayed alive and their candles kept burning in closed bell jars.

Priestley taught Lavoisier how to obtain what he took to be measurements of the phlogiston content of an unknown gas. A sample of the gas to be tested is run into a graduated tube filled with water and inverted over a water bath. Priestley, who thought there was no such thing as oxygen, believed the change in water level indicated how much phlogiston the gas contained.

Lavoisier reported observing the same water levels as Priestley even after he abandoned phlogiston theory and became convinced that changes in water level indicated free oxygen content Conant74— The moral of these examples is that although paradigms or theoretical commitments sometimes have an epistemically significant influence on what observers perceive, it can be relatively easy to nullify or correct for their effects.

How do observational data bear on the acceptability of theoretical claims? They then try to explain how observational data argue for or against the possession of one or more of these virtues. One way to decide whether a theory or a theoretical claim is true, close to the truth, or acceptably probable is to derive predictions from it and use observational data to evaluate them.

Hypothetico-Deductive HD confirmation theorists propose that observational evidence argues for the truth of theories whose deductive consequences it verifies, and against those whose consequences it falsifies Popper32— But laws and theoretical generalization seldom if ever entail observational predictions unless they are conjoined with one or more auxiliary hypotheses taken from the theory they belong to.

When the prediction turns to be false, HD has trouble explaining which of the conjuncts is to blame. If a theory entails a true prediction, it will continue to do so in conjunction with arbitrarily selected irrelevant claims.

Ignoring details, large and small, bootstrapping confirmation theories hold that an observation report confirms a theoretical generalization if an instance of the generalization follows from the observation report conjoined with auxiliary hypotheses from the theory the generalization belongs to. Observation counts against a theoretical claim if the conjunction entails a counter-instance.

Here, as with HD, an observation argues for or against a theoretical claim only on the assumption that the auxiliary hypotheses are true Glymour— Bayesians hold that the evidential bearing of observational evidence on a theoretical claim is to be understood in terms of likelihood or conditional probability. For example, whether observational evidence argues for a theoretical claim might be thought to depend upon whether it is more probable and if so how much more probable than its denial conditional on a description of the evidence together with background beliefs, including theoretical commitments.

According to all of them it can be reasonable for adherents of competing theories to disagree about how observational data bear on the same claims.

Scientific theory - Wikipedia

As a matter of historical fact, such disagreements do occur. The moral of this fact depends upon whether and how such disagreements can be resolved. Because some of the components of a theory are logically and more or less probabilistically independent of one another, adherents of competing theories can often can find ways to bring themselves into close enough agreement about auxiliary hypotheses or prior probabilities to draw the same conclusions from the evidence.

Theories are said to save observable phenomena if they satisfactorily predict, describe, or systematize them. How well a theory performs any of these tasks need not depend upon the truth or accuracy of its basic principles. In particular, the assumption that the planets rotate around the sun must be evaluated solely in terms of how useful it is in calculating their observable relative positions to a satisfactory approximation.

For Duhem a physical theory …is a system of mathematical propositions, deduced from a small number of principles, which aim to represent as simply and completely, and exactly as possible, a set of experimental laws. Investigators produce them by performing measuring and other experimental operations and assigning symbols to perceptible results according to pre-established operational definitions Duhem For Duhem, the main function of a physical theory is to help us store and retrieve information about observables we would not otherwise be able to keep track of.

Theorists are to replace reports of individual observations with experimental laws and devise higher level laws the fewer, the better from which experimental laws the more, the better can be mathematically derived Duhem21ff.

scientific theories must meet the following criteria are supported

Let EL be one or more experimental laws that perform acceptably well on such tests. Higher level laws can then be evaluated on the basis of how well they integrate EL into the rest of the theory.

Other data may need to be accommodated by replacing or modifying one or more experimental laws or adding new ones. If the required additions, modifications or replacements deliver experimental laws that are harder to integrate, the data count against the theory. If the required changes are conducive to improved systematization the data count in favor of it. Data and phenomena It is an unwelcome fact for all of these ideas about theory testing that data are typically produced in ways that make it impossible to predict them from the generalizations they are used to test, or to derive instances of those generalizations from data and non ad hoc auxiliary hypotheses.

That is because precise, publicly accessible data typically cannot be produced except through processes whose results reflect the influence of causal factors that are too numerous, too different in kind, and too irregular in behavior for any single theory to account for them. When Bernard Katz recorded electrical activity in nerve fiber preparations, the numerical values of his data were influenced by factors peculiar to the operation of his galvanometers and other pieces of equipment, variations among the positions of the stimulating and recording electrodes that had to be inserted into the nerve, the physiological effects of their insertion, and changes in the condition of the nerve as it deteriorated during the course of the experiment.

Hill, walking up and down the stairs outside of the laboratory. To make matters worse, many of these factors influenced the data as parts of irregularly occurring, transient, and shifting assemblies of causal influences.

With regard to kinds of data that should be of interest to philosophers of physics, consider how many extraneous causes influenced radiation data in solar neutrino detection experiments, or spark chamber photographs produced to detect particle interactions. The effects of systematic and random sources of error are typically such that considerable analysis and interpretation are required to take investigators from data sets to conclusions that can be used to evaluate theoretical claims.

This applies as much to clear cases of perceptual data as to machine produced records. When 19th and early 20th century astronomers looked through telescopes and pushed buttons to record the time at which they saw a moon pass a crosshair, the values of their data points depended, not only upon light reflected from the moon, but also upon features of perceptual processes, reaction times, and other psychological factors that varied non-systematically from time to time and observer to observer.

No astronomical theory has the resources to take such things into account. Similar considerations apply to the probabilities of specific data points conditional on theoretical principles, and the probabilities of confirming or disconfirming instances of theoretical claims conditional on the values of specific data points.

Instead of testing theoretical claims by direct comparison to raw data, investigators use data to infer facts about phenomena, i. The fact that lead melts at temperatures at or close to Theories that cannot be expected to predict or explain such things as individual temperature readings can nevertheless be evaluated on the basis of how useful they they are in predicting or explaining phenomena they are used to detect.

The same holds for the action potential as opposed to the electrical data from which its features are calculated, and the orbits of the planets in contrast to the data of positional astronomy.

For many purposes, theories that predict and explain phenomena would be more illuminating, and more useful for practical purposes than theories if there were any that predicted or explained members of a data set. Suppose you could choose between a theory that predicted or explained the way in which neurotransmitter release relates to neuronal spiking e. For most purposes, the former theory would be preferable to the latter at the very least because it applies to so many more cases.

For most purposes, these would be preferable to a theory that predicted specific descriptions in a case history. In view of all of this, together with the fact that a great many theoretical claims can only be tested directly against facts about phenomena, it behooves epistemologists to think about how data are used to answer questions about phenomena.

Lacking space for a detailed discussion, the most this entry can do is to mention two main kinds of things investigators do in order to draw conclusions from data. The first is causal analysis carried out with or without the use of statistical techniques. The second is non-causal statistical analysis. First, investigators must distinguish features of the data that are indicative of facts about the phenomenon of interest from those which can safely be ignored, and those which must be corrected for.

Sometimes background knowledge makes this easy. Under normal circumstances investigators know that their thermometers are sensitive to temperature, and their pressure gauges, to pressure. An astronomer or a chemist who knows what spectrographic equipment does, and what she has applied it to will know what her data indicate. When Ramon y Cajal looked through his microscope at a thin slice of stained nerve tissue, he had to figure out which if any of the fibers he could see at one focal length connected to or extended from things he could see only at another focal length, or in another slice.

Analogous considerations apply to quantitative data. It can be harder to tell whether an abrupt jump in the amplitude of a high frequency EEG oscillation was due to a feature of the subjects brain activity or an artifact of extraneous electrical activity in the laboratory or operating room where the measurements were made.

The answers to questions about which features of numerical and non-numerical data are indicative of a phenomenon of interest typically depend at least in part on what is known about the causes that conspire to produce the data. Statistical arguments are often used to deal with questions about the influence of epistemically relevant causal factors. For example, when it is known that similar data can be produced by factors that have nothing to do with the phenomenon of interest, Monte Carlo simulations, regression analyses of sample data, and a variety of other statistical techniques sometimes provide investigators with their best chance of deciding how seriously to take a putatively illuminating feature of their data.

But statistical techniques are also required for purposes other than causal analysis. To calculate the magnitude of a quantity like the melting point of lead from a scatter of numerical data, investigators throw out outliers, calculate the mean and the standard deviation, etc. Regression and other techniques are applied to the results to estimate how far from the mean the magnitude of interest can be expected to fall in the population of interest e.

The fact that little can be learned from data without causal, statistical, and related argumentation has interesting consequences for received ideas about how the use of observational evidence distinguishes science from pseudo science, religion, and other non-scientific cognitive endeavors.

To find epistemically significant differences, one must carefully consider what sorts of data they use, where it comes from, and how it is employed. The virtues of scientific as opposed to non-scientific theory evaluations depend not only on its reliance on empirical data, but also on how the data are produced, analyzed and interpreted to draw conclusions against which theories can be evaluated. Data are produced, and used in far too many different ways to treat informatively as instance of any single method.

Thirdly, it is usually, if not always, impossible for investigators to draw conclusions to test theories against observational data without explicit or implicit reliance on theoretical principles. This means that when counterparts to Kuhnian questions about theory loading and its epistemic significance arise in connection with the analysis and interpretation of observational evidence, such questions must be answered by appeal to details that vary from case to case.

Their diversity is a reason to doubt whether general philosophical accounts of observation, observables, and observational data can tell epistemologists as much as local accounts grounded in close studies of specific kinds of cases. Logic and the rest seem unable to deliver satisfactory, universally applicable accounts of scientific reasoning.

But they have illuminating local applications, some of which can be of use to scientists as well as philosophers. Princeton University Press,pp. Oxford Univesity Press, Bogen, J, and Woodward, J. Kessinger reprint of edition. University of Chicago Press. Harvard University Press, pp. Princeton University Press, Cambridge University Press,pp.

Hacking, I,Representing and Intervening, Cambridge: University of Chicago Press,pp.

The Structure of Scientific Theories

Frank Cass and Company, University of Chicago Press, reprinted, Neurath, Philosophical Papers, Dordrecht: Copernicus On the Revolutions, E. Johns Hopkins University Press,p. Synthesizing Proteins in the Test Tube, Stanford: Philosophical Library,pp. The Structure of Scientific Theories, Urbana: