The first 4 chapters in Reynolds Textbook give a clear insight on what composes a scientific knowledge. The author gives a simple ground for researchers supported with detailed examples for better understanding of Scientific Knowledge. Reynolds emphasized on the characteristics that differentiate between what can be considered a scientific knowledge and what can not. Any concept can be categorized as a scientific knowledge if it has certain feature, those features are:

 

1.The degree of its abstractness (it should be independent of time and space)

2.Whether there is intersubjectivity among relevant scientists for the relationship among the concepts included in the sentence which wont be attained without their agreement on their meaning in the first place. It is not expected to gain agreement from all the audience or readers, but there should be agreement among those who have similar experience with that concept.

3.Whether there is a relevancy between the concepts and empirical research results.

 

As for chapter two, Reynolds differentiated various types of new ideas (Kuhn Paradigm or “scientific revolution”, Paradigm, Paradigm variations) with providing example for each type and what are the features for each type. It can be indicated that Kuhn paradigm is the one that makes a theatrical change from previous orientation. However, Reynolds gave examples for pure fields like his example for Freud in psychology. I was hoping to see an example for interdisciplinary field.

 I was wondering, in case of interdisciplinary field,  is there a possibility to categorize an idea as Kuhn Paradigm in one field and at the same time it goes under Paradigm type for another field?? Just wondering!! 

Reading Chapter 3 makes me differentiate between the 4 forms for concept quantifications (nominal, ordinal, interval, and ratio), and I have learned that the sets of procedures that are used to measure, if the theoretical concept exists or not, are the operational definitions and they should also be independent of time and space.

 Reynolds defines in chapter 4 the different kinds of statements and the sub kinds within. What I understood from this chapter, that there are two main kinds of statements (Existence and Relational statements). The existence statement is the one which claims that a concept exists, while the relational statement is a statement that describes a relationship between two concepts within the statement itself. I have learned from this chapter the difference between the definitions and the existence statements. The definition describes the concept itself, while the existence statement claims the concept’s existence. Moreover, I knew that relational statement is the heart of the scientific knowledge. Relational statements can be categorized into two groups (association, and causal statements). Furthermore, I have learned the difference between the various types of theoretical statements (laws, axioms, hypothesis, propositions, and empirical generalization) and this issue wasn’t clear to me before reading that chapter.

Reynolds in the second part of his book underlines the three forms of theory (Set of laws, Axiomatic, and Casual Process). He also points out to the advantages and disadvantages of each form. What can be indicated from chapter 5, that not all the three forms are suitable for the purposes of scientific knowledge (Topology, Description and Explanations, Control, and sense of understanding). Set of laws and axiomatic forms are appropriate for the categorizing, describing and explaining, and control purposes of scientific knowledge. However, they don’t give a sense of understanding. On the other hand, causal process form is the only form that succeeded to provide a sense of understanding beside the other three purposes.

 

In chapter 6, Reynolds discusses how we can test a theory. Theory’s usefulness evaluation should be based on the degree to which the statement is corresponding to the empirical research’s results. Also, Reynolds explains how empirical research’ results can change the individual’s confidence attitude towards a statement, and he also emphasizes that the degree of changing confidence in the statement depends of three main factors:

 

1. The scientist’s attitude toward the statement before revealing the empirical research’ results
2. The quality of research’s procedures
3. The results of the research itself.

 

Reynolds differentiates in chapter 7 between two strategies (Theory-then-Research strategy, and Research-then Theory strategy) for developing a body of scientific knowledge, and he also explains their downsides. At the beginning of this chapter, I was confused about the difference between the two strategies, but the author gives a clear superb example for that difference, and after I reached his Dennis the Menace example, it was so easy for me to see the difference. What I understood from his example is that research-then-theory strategy (Bacon’s strategy) states that there are real patterns in nature and the scientist should find those patterns. On the other hand, theory-then-research strategy is the strategy in which scientist entails his description on the phenomenon. Yet, I’m still confused about Composite Approach and how this approach brings together the advantages of the two strategies mentioned above. It is not that clear to me. I need to reread this part again for better understanding.

In my opinion, the central theme of Reynolds' book is to teach the concepts necessary for us to conduct research in a manner to convey a sense of understanding (about the phenomena of interest) to our readers and the scientific community to which we belong.

Maybe, I have oversimplified—after all, the book also emphasized other purposes of research, namely, providing typologies, logical explanations, and predictions. However, when a choice has to be made in the course of a research, the book advises on the options that provides a sense of understanding in addition to typologies, explanations, and predictions. It is basically for this reason that the causal form of theory is preferred. Also, when choosing between theories, the criteria should be those theories that provide better sense of understanding, and not simplicity.

Writing in a manner to contribute to scientific knowledge requires grounded familiarity with the concepts and ideas that the scientific community adopts as useful for the aforementioned purposes of science. Such familiarity allows us, as researchers, to present our ideas as clearly as possible.

If the research is about developing theories, then the statements making up the theory should be in the causal process form. When researching on the relationships between constructs, researchers often have to choose among different theories relating to the constructs of interest. Reynolds advises that the criterion for choosing in such cases should not be simplicity, but rather the theories that provide the greatest sense of understanding. Theories are abstract conceptions, neutral of time and space. As such they cannot be proven true, since some researchers can later prove them false. However, the concrete statements we derive from them and other abstract statements can be proved true or false. Herein lies the value of empiricism in the research process. Thus, while the contributions to science are in the form of abstract statements of relationships, those contributions must be based on our deriving suitable concrete and measurable statements and developing acceptable research programs, treatments, measures and observation.

When presenting our ideas, we should bear in mind that other scientists will be evaluating the quality and validity of our research. The whole issues of conclusion, internal, construct, and external validity are brought to bear in assessing whether the claims we have made are acceptable to the community of scientists. To this end, Reynolds advises that good research design, the development of clear and inter-subjective operational definitions of the abstract concepts of our interest, and asking important research questions are very important research procedures that should take precedence even over statistical procedures. This is not to say that statistics is unimportant, if the research design, operationalization of constructs, and research questions are not done right, then it would not matter how sophisticated your research procedures are. You will have been precisely measuring the wrong thing.

Reynolds is the classic it is mainly because of the concise manner in which it has provided "a sense of understanding" of the conceptual issues involved in the academic research process.

I think Chapter 5, Forms of Theories, is a key chapter in the rest of this book. I learned three different conceptions of how sets of statements should be organized so as to constitute a theory. These are set-of-laws, axiomatic, and causal process. I especially concentrated on this part to understand each example and distinguish the difference between conceptions.  In terms of set-of-laws author insists that if scientific knowledge is organized in the form of a set of laws, a scientist cannot achieve all the purpose of science, since he cannot provide a sense of understanding. The axiomatic theory is defined as an interrelated set of definitions and statements and one of the most important problems in dealing with theories in axiomatic form is determining how to select the axioms. Also it can provide a sense of understanding, but not always. The causal process form has the major difference between this form of theory and the axiomatic form. That is all statements are considered to be of equal importance. Author also indicates that while a conception of theory as a set of laws will lead to an efficient use of resources if the research-then-theory strategy is employed, a theory in axiomatic or causal process form will lead to an efficient use of resources if a theory-then-research strategy is employed.

Another interesting part is a comparison between strategies, research-then-theory and theory-then-research, in chapter 7. Author explains that research-then-theory strategy has the disadvantage that considerable effort may be spent on collecting data that have no useful purpose, but it may provide some information useful for inventing theories and theory-then-research strategy also has the disadvantage that the scientist may have no initial information on which to base the first attempts at a theory, but research is more efficient when one only collects information related to a few important hypotheses.

Chapter 8, conclusion, is very good to remember key points in this book. While I read this chapter I could arrange whole procedure to constitute a theory with abstract theoretical statements.

Reynolds’ Chapter 6 is equally important as Chapter 5. However, it is difficult to cramp information about “testing theories” in one chapter, even for an overview. Nonetheless, Reynolds provides valuable points regarding attitudes toward theory testing.

 

When we do a research to support a concept, the research is always conducted under specific temporal and spatial settings. Then we use the empirical data collected in a specific time and space as our evidence to support an abstract concept which is supposed to be irrelevant to specific time and space. Reynolds points out: “How can an abstract statement be proven true?”

 

If a statement is abstract, it must be applicable in the future. If it is applicable to future situations, the possibility exists that it might be proven false in the future. However, since abstract statements are also applicable to the past and the present, it is possible to prove that they do not apply to some situations. As a researcher, we need to bear in mind that it is impossible to prove an abstract statement true, but it is possible to prove it false. When we apply statistics to our research, we either “fail to reject” or “reject” the hypothesis. We never say the hypothesis is “true”. 

One of the greatest danger of research, or perhaps any human reasoning, is the inferential leap from sample data to population fact. Inferential leaps are constantly made in politics, economics, education, and other areas of large concern. If the government cuts interest rates, inflation will increase, for example. Scientists, too, make inferential leaps- often very large ones- with one important difference: The scientist is aware of such leaps and that such leaps are always risky.

Two kinds of reading items stand out to me in these two chapters: three forms of theories and statistical inference: in search of the ‘truth’.

Forms of Theories

Set-of- Laws form. This form is well tested and supported by empirical research having concepts with operational definitions in concrete settings. Most of the set-of-laws theories explain causal relationship between two concepts.

Axiomatic form. This is a system of definitions, existence statements, axioms, and propositions specially derived to explain the theory.

Causal process form. This also is a system of definitions, existence statements, and causal statements that systematically explain the theory.

Though all these forms achieve the first three objectives of science, which are to provide typology, explanation, and control, however, only the causal process form could provide a sense of understanding. Therefore, it is novel to strive to seek causal links between concepts as much as possible.

Statistical Inference: In search of the ‘truth’

When we hypothesize, it is important to care for Type I (α) and II (β) errors. Type I relates to rejecting null hypothesis when it is true while Type II bothers on rejecting an alternate hypothesis when it is true. Reynolds advice that we should keep these error levels very low.

My question is: what are best practices employed in research to keep these two errors low?

There are a lot of terms in these chapters. The words structure, model, and paradigm are troublesome because they are hard to define clearly and unambiguously. A “paradigm” is also a model, an example. Diagrams, graphs, and verbal outlinesare are also paradigms. “Model” has another meaning in the testing of theory using multivariate procedure.

 

In Godfrey-Smith's book, he follows a narrower use of the term “model”. In this sense, a model is a structure (abstract or concrete) that is used to represent some other system. However, what is a structure? To me, a structure is the framework, organization, or configuration of elements related in specified ways.

  

 In Kuhn's book, the term “paradigm” is used in several different ways. Kuhn agreed that he had used the word ambiguously, and throughout his career he kept finetuning it. In Godfrey-Smith's book, he recognizes two different senses of the term “paragigm”. One is a narrower sense (a major scientific achievement or examplar), and the other is a broader sense (a whole new way of doing science). It is interesting that the term “paragigm shift” has been popular regardless of its ambiguity.

Godfrey-Smith’s book is an introduction to the philosophy of science, focusing mainly on epistemological concerns of how observational evidence can justify a scientific theory and whether we have any reason to hope that science can succeed in describing the world as it really is. The book takes a historical approach to explaining concepts as well as the contextual issues and people that influenced their acceptance and rejection.

Chapter 1 points out that to understand science, we need to distinguish it from other kinds of investigation of the world. It points out that although people construe science differently—some broadly, others narrowly, and others in a way that lies in between—we ultimately need to acquire (1) a general understand how humans gain knowledge of the world, and (2) an understanding of what makes the work descendent from the Scientific Revolution different from other areas of human interest.

We learn that philosophers haven’t always agreed as to the right form of a philosophical theory of science. Nor do they agree on the right questions to be asking. For us to get a handle on science and how it works, we are introduced to three concepts that have been advanced as distinguishing tools of science: empiricism, mathematics, and social structure of scientific communities. Instead of seeing them as rival ideas, Godfrey advises that we see them as pieces of a more complete answer as to how science works and what makes it distinctive.

The second chapter dwells on the empiricist tradition, tracing its history up to the fall of logical positivism. It talks of Hume’s skepticism, Mach’s phenomenalism, Kant’s reconciliation of rationalism and empiricism, Hegelianism, Heidegger’s metaphysics, and the Vienna Circle.

The verifiability theory of meaning and the analytic-synthetic distinction are emphasized along with other positivist doctrines. Godfrey-Smith does a service exploring how early 20th century positivists were influenced by criticisms to modify their ideas.

Chapter #3 deals with the philosophical problems of induction and confirmation. Induction which is the positivists' approach to linking observations to theory, requires confirmation to be valid. Two broad kinds of logic are introduced: deductive and inductive. Deductive logic is the better understood and less controversial kind of logic. But it has limitations for the logical empiricists. This is because, as empiricists, they looked to particular observations and occurrences for support of generally couched theories. Conclusions from such observations cannot, under deductive logic, be valid as long not all possible observations have been made. (All it takes is just one non-confirming observation to prove a generalization false) Thus, non-deductive approaches to logic are used to link observations to theories. Even though the term inductive logical is used to generally to refer to all non-deductive logic, strictly speaking there are three forms of non-deductive logic—induction, projection, and explanatory inference. Induction here refers to inferences from particular observations in support of generalizations. Projection, closely related to induction, refers to inferences from particular observations in prediction of the next case. Explanatory inference is inference drawn from particular observations in support of some hypothesis. The problem of analyzing confirmation includes all these considerations. It uses the Ravens Problem and Goodman’s New Riddle of Induction to buttress the difficulties inherent in the theory of confirmation.

The fourth chapter is devoted to Popper’s philosophy of science. Popper was interested in the problem of demarcation, i.e., the problem of distinguishing science from non-science. He dubbed the proposed solution “falsificationism”—the claim that a hypothesis is scientific if and only if it has the potential of being refuted by some possible observation. His claim was that all testing in science was to refute theories through observations. For him, confirmation is a myth. As such, he severely criticized the logical empiricists in their attempt to develop a theory of confirmation or (inductive logic). Proper uses his idea of falsification to propose a theory of scientific change—the theory that science changes by way of a two-step cycle that repeats endlessly. The first is conjecture, when a scientist proposes a hypothesis; and the second is attempted refutation, during which stage the hypothesis is to critical testing in an attempt to prove it false. Once the hypothesis is refuted, the cycle starts all over again, with a new conjecture proffered. But when an observation refutes a theory, how certain are we that we measured the right thing and that we measured it right? Godfrey-Smith explains how criticism of Popper has caused him to shift to a position that implies that falsification can occur without it being backed by a deductive logical relation between observation and theory.

Chapter 5 deals with Kuhn’s conception of normal science. He distinguishes normal science as work occurring within a paradigm and aimed at extending and refining that paradigm. For Kuhn, there are two distinct kinds of scientific change—change within normal science (paradigm shift), and revolutionary science (Kuhn’s paradigm). Concepts, such as justification, rationality and progress, apply differently to normal and revolutionary science. Under normal science, the fundamental ideas associated with a paradigm are not debated. Scientists strive to extend the paradigm, theoretically and experimentally, in order to deal with new cases. The rejection of a paradigm happens only when a critical mass of anomalies has arisen; and a rival paradigm has appeared.

 

This book is an introduction of philosophy of science and explains many issues in science field. Also most of the issues introduced in this book are epistemological issue which is the side of philosophy that is concerned with questions about knowledge, evidence, and rationality.  This book suggests three ideas, which are different answers to general questions about how science works. These are empiricism, mathematics and science, and social structure and science.

Logical positivism and its problems are explained in chapter 2 and problem of understanding how observations can confirm a scientific theory is discussed in chapter 3. Chapter 4 introduces Karl Popper whose theory of science has been criticized a great deal by philosophers over the years. The debating between Popper’s theory and kuhn’s theory are well discussed against various point of view. While Popper insists science is characterized by permanent openness, a permanent and all encompassing critical stance, even with respect to the fundamental ideas in a field, Kuhn refutes Popper’s insertion indicating that science doesn’t exhibits a permanent openness to the testing of fundamental ideas.

Also Kuhn argued that observational data and logic alone cannot force scientists to move from one paradigm to another, since different paradigms often contains different rules for treating data and assessing theories. Hence he insists a scientific revolution occurs when one paradigm breaks down and is replaced by another.

In addition Kuhn’s theory sometimes the rejection of a paradigm happens only when a critical mass of anomalies has arisen and a rival paradigm has appeared. For this anomaly Kuhn explains that this is a like puzzle that has resisted solution. So normal science needs to proceed as usual and scientists need to regard them as a challenge. I agree with Kuhn’s insertion that there exist a lot of paradigms in the world and all of paradigms are breakable like a bomb.

In the introductory chapter in Smith textbook “Theory and Reality”, Smith gives a grand tour over historical periods of time to deliver a clear message for the readers about what philosophy of science is, and what makes science special than any other human thoughts about the universe. Endeavor to ask and answer the main questions about the universe is philosophy according to smith. The author also distinguishes between two types of theories, descriptive and normative theories. Descriptive theory is trying to describe things without adding judgments to it. For normative theories, theorists add value judgments to things. The author attempts to answer the question how the science works and what should be taken as science and what should not. The author gives three main ideas (Empiricism, Mathematics and science, and science’s social structure) to show how science can be special. Empiricists’ belief is that experience is the source of knowledge. Empiricists don’t have full trust in others; they want to test everything by themselves. According to my understanding, experience can contribute to building the knowledge, but it shouldn’t be taken as final source to judge phenomena, because it is not necessary that all people have the same experience with the same subject. Even their perceptions to the phenomenon vary. At this point mathematics can play important role in quantifying patterns within events. This was the second idea smith gives in attempting to answer how science works question. However, not all fields dependent on mathematical tools as illustrated by smith. The third idea given by Smith is the social structure of science. Smith emphasizes on trust and cooperation necessity to the science.  Smith differentiates between the logical positivist (Vienna Circle) and logical empiricism in chapter 2. In brief, Logical positivist views about science and knowledge are based on a general theory of language that featured two main ideas analytic-synthetic distinction and verifiability theory of meaning. Logical empiricism aimed to develop a logical theory of evidence and confirmation. I’m still having hard times in understanding the general theory of language though. Smith states that there are problems in the logical positivists’ central ideas, such as, the holistic theory of testing and having well formulation for variability principle. Moreover, Smith distinguishes between two types of logic, deductive and inductive logic. Deductive logic means if an argument has true premises, then the conclusion should be true. However, Inductive logic refers to the arguments that support its conclusions, but it doesn’t give the guarantee that is given in the deductive logic. The author also discusses further kinds of logic in chapter 3 as well. Smith states that logical empiricism overcame the problem of theory confirmation by two methods. First, they formulated an inductive logic that looked as much as possible as deductive logic. Second, they applied the mathematical theory of probability. The author then explains Ravens example and other examples to give a sense for readers about the nature of confirmation problem. However, I’m confused about raven example, and I didn’t get much of the ideas the author wanted to deliver. I was hoping to see simple examples instead of using letters like G and A. I really found it confusing to follow the example in a simple way, beside I also wanted to see summaries at the end of each chapter like the previous readings we had, but it didn’t happen in this book.  Unfortunately, I didn’t finish reading chapters 4 and 5 yet. I prefer commenting on those two chapters when I finish reading them.

Godfrey-Smith shows how the philosophy of science has evolved over time by examining various worldviews, their relevance and pitfall and how they shaped the traditions of science today. Major philosophical traditions include empiricism, logical empiricism, induction and confirmation in chapters 1-3.

Empiricism holds that knowledge is obtained only from experience thereby excluding knowledge from unobservable phenomenon. But direct empirical tests do not guarantee success. Empiricism evolves from the middle ages through to 17th century. Facts obtained from direct contact with or observation of the world remains the fundamental body of evidence in support of scientific theory.

Logical Positivism holds that knowledge is obtained through deductive reasoning from systems of logical statements. Logical positivism evolved out of the work of Auguste Comte. ‘Analytic-synthetic’ and ‘verifiability theory of meaning’ were added to logical empiricism portfolio by Immanuel Kant. A statement is analytic if it contains non-factual contents as in mathematical logic or synthetic if it contains factual contents. That factual contents are observable or verifiable. Today, deductive logic enables one to state premises or claims and deduce conclusion. However, logical empiricism could not fully develop the induction logic.

Logical Empiricism holds that knowledge cannot only be deduced but induced. Examples of non-deductive inferences include induction, explanatory inferences, and projection. Curve fitting is one of the current applications of inductive logic. Scientists need to support choice of curve because potential curves are infinite in number.

Post-positivism holds the falsification principled posed by Karl Popper.Falsification principle states that a scientific theory is a theory whose predictions can be empirically falsified (or shown to be incorrect). According to this principle, hypotheses should be subjected to falsification scrutiny (as with the T test statistics for example); this is the underlying assumption behind quantitative analysis. Some of these statistics are employed to provide quantitative estimates of the hypothesised relationship among the variables under study.

Topic 1: Adopting GIS in Healthcare for new health service.

Claim: Adoption of GIS by healthcare system can create more valuable health services.

Research question : How GIS affects acceptance of new health service for customer?

                           Can GIS affects creating new health service ?

Research method : Questionnaire

Topic 2: Comparison between Agile development and traditional development

Claim: Find out each strength and weak part by comparison then suggest reciprocal complement way of development.  

Research question : How these two development ways are different?

                           What are the strength and weak part of each development way?

Research method : literature review

Naturalism is often summarized as “philosophy should be continuous with science”. Based on this idea, naturalism believes that there should be some link between scientific theories and philosophical theories. Philosophy can use the results from the sciences to help resolve philosophical questions. Naturalism is also said to be opposed to foundationalism. The author suggests that naturalism is our best hope for solving the core problems of philosophy of science.

 

Naturalism is a theory that relates scientific method to philosophy by affirming that all beings and events in the universe are natural. Consequently, all knowledge of the universe falls within the realm of scientific investigation.

 

Naturalism presumes that nature is in principle completely open to human being's investigation. There is in nature regularity, unity, and wholeness that imply objective laws, without which the pursuit of scientific knowledge would be absurd. Human being's endless search for concrete proofs of his beliefs is seen as a confirmation of naturalistic methodology. Naturalists point out that even when one scientific theory is abandoned in favor of another, man does not despair of knowing nature, nor does he repudiate his search for truth..

 

While naturalism has often been equated with materialism, it is much broader in scope. Materialism is indeed naturalistic, but the converse is not necessarily true. Strictly speaking, naturalism has no ontological preference. So long as all reality is natural, no other limitations are imposed. Naturalists have in fact expressed a wide variety of views.

 

Only rarely do naturalists give attention to metaphysics (which they deride), and they make no philosophical attempts to establish their position. Naturalists simply assert that nature is reality, the whole of it. There is nothing outside the nature.

My sense of the general flow of the book is that starting from Kuhn, there was a break from earlier logical theories of science (confirmationism and falsificationism) to theories that are based on what the actual practice of science is. Moreover, instead of talking mainly about theories, we talk paradigms, research programs, and research traditions.

Kuhn tells us what scientists do during normal science and during revolutionary science. During normal science, the members of the scientific community employ the dominant paradigm as a tool for solving outstanding problems, neither questioning nor seriously testing it. During a period of revolutionary science, the problem solving ability of the paradigm is being called to question and the scientific community actively debates its underlying principles as well as those of its rivals. Thus, the usual routine of problem solving is suspended until a new paradigm establishes dominance. But how does this new paradigm accomplish this? If I could borrow from the tenacity with which they themselves (Kuhn, Popper, Lakatos, etc) hold and defend their theories, then scientific revolutions will likely extend for long periods of time before things will normalize.

Lakatos talks of research programs as a sequence of theories characterized by a hard-core (the distinguishing features theories must have to be included in the research program) and the protective belt (the features of theories that are alterable without excluding such theories from the research program). Changes within a research program are by way of alterations to the protective belt. The protective belt may be altered in order to make the program more realistic; or it could be altered when the program starts to make false predictions. If an alteration not only fixes the problem at hand but also allows the research program to make a novel prediction, then the alteration is said to be progressive. If the alteration does not lead to any novel predictions, then it is regarded as degenerating.Lakatos states that a research program is in good health as long as a sufficient number of the alterations to it are progressive. Conversely, it has problems when there are too many alterations that are degenerating. Lakatos advises that once a research program is sufficiently degenerate, and once there is a progressive research program to take its place, the degenerate program should be jettisoned. However, Lakatos does not provide any metrics for degeneracy, nor does he define a threshold beyond which degeneracy becomes fatal to a research program.

Godfrey-Smith has a lot of good things to say of Laudan. Laudan’s prescription for us is that we should accept the research tradition that has solved the most problems and pursue the one that is currently solving the most problems. Simply put, sciences progresses by solving problems. Cool. But I have this problem:

Kuhn’s paradigm, I know;
Lakatos’s research program, I know;
Laudan’s research tradition, I’m yet to know!
Who, out there, will let me know?

 

Smith presents in the second part of his book “Theory and Reality” different views of science for different scientists. Also, Smith explains the differences and similarities among those multiple views. Smith addresses Kuhn view for scientific revolution. Kuhn believes that there should be a crisis before the revolution, not only a crisis, but there should be an acceptance of a new paradigm so people leave the old paradigm. Acceptance comes when there is a feel of new paradigm’s importance. This is what refers to the falsification or rejection. Scientists won’t reject a paradigm without witnessing the new paradigm’s contributions to the unsolved problems. According to Kuhn, there should be gains and losses accompanied with revolution. However, to answer whether gains overweight losses or vice versa without biases is hard. This is attributed to the incommensurable problem which stated by Kuhn. Kuhn believes that people from different paradigms can not communicate because they use different language and terms. Also, if the communication is valid, those people use different standards of arguments. Kuhn categorizes the scientific change into two forms, orderly change within normal science and revolutionary change. From this point, Kuhn views science as a relativist.

  

Other philosophies came as responses to Kuhn’s view. Lakatos saw that Kuhn’s ideas are dangerous to the science rationality. Lakatos emphasized on the importance of historical case studies, and he claimed that those studies are of high importance in assessing in views of science. Lakatos presented a new view of science which is based on research programs. Lakatos rejected the idea that each field should have one paradigm as was emphasized by Kuhn. However, Lakatos stated that there can be different research programs within the same field, and competition should take place among research programs. According to Lakatos, research program consists of two main components, the hard core and the protective belt. Lakatos emphasized on two kinds of scientific change, change within the scientific program and higher level change. As indicated by Lakatos, research program can be progressive or degenerating. What I understood from this part that progressive program is the one that extends the research programs and takes it to another higher level. On the other hand, degenerating research program is the one to which changes don’t extend it to other cases. However I was wondering!

 How can scientists judge if a research problem is progressive or degenerating prior any effort to change its program?   

Laudan developed in his book “progress and its problem, 1977” another structure for science which is close to Lakatos. Conversely, Laudan called the research programs as research traditions. Laudan viewed that theories are grouped within the research traditions are tightly related, and theories included in one research tradition can be included in another opponent research tradition. Also, Laudan accentuated on two types of attitudes (acceptance and pursuit) towards the theories. Smith also presented Feyerabend’s view of science. From Feyerabend’s point of view, science is a matter of creativity, and scientist shouldn’t be restricted by rules and constraints in science.

Chapter 6 discussed Kuhn and revolutions in detail. Kuhn indicates that large-scale scientific change usually requires both a crisis and the appearance of a new candidate paradigm. Also crisis alone will not induce scientists to regard to large-scale theory or paradigm as falsified. Author found two kinds of scientific change from the Kuhn’s picture. One is a change within normal science which is orderly and responsive to evidence, but normal science works via a closing of debate about fundamental ideas. The other change is a revolutionary change which does involve challenges to fundamentals, but these are episodes in which the orderly assessment of ideas breaks down. Author also emphasizes sudden appearance of problem-solving power is the spark to the revolution and this display has a key role in these fundamental transitions between paradigms. In addition Kuhn says the idea that different paradigms in a field are incommensurable with each other.  It is because people in different paradigms will not be able to fully communicate with each other and will use different standards of evidence and argument.

Chapter 7 introduces different philosophical accounts of science developed by Lakatos, Laudan, and Feyerabend. Also it is developed in interaction with Kuhn or in response to him. The main contribution of Lakatos was the idea of research program which is similar to Kuhn’s paradigm. However we can expect to find more than one research program in a scientific field at any given time and the large-scale processes of scientific change should be understood as competition between research programs. In Laudan’s stand point, he called these large-scale units of scientific work research traditions rather than research program. Feyerabend was influenced by Popper and argued for epistemological anarchism. The epistemological anarchist is opposed to all systems of rules and constraints in science. Also great scientists are opportunistic and creative, willing to make use of any available technique for discovery and persuasion.

Chapter 8 explains about sociology of science. Robert Merton was a founder of this field and the central figure for many years. Mertonian sociology of science is mainstream sociology applied to the structure of science and to its historical development. He isolated norms of science which is a set of basic values that govern scientific communities. These norms include universalism, communism, disinterestedness, and organized skepticism. Feminism and science studies are discussed in Chapter 9. Here author indicates that science itself, and main stream theorizing about science and knowledge have helped to keep women in a second-class position as thinkers, knowers, and intellectual citizens.

Kuhn is understood to have implied the concept ‘paradigm’ in both narrow and broad senses. Paradigm in the narrow sense is an achievement that becomes an inspiration and direction for further works. While paradigm in the broad sense is a package of claims about the world, methods of collecting and analyzing data, and habits of scientific thoughts and action. This later sense subsumed the former.

He posits that a scientific field has one overriding paradigm per field per time. And that the orderly cooperation and openness of normal science nurtures a consistent progression in scientific knowledge. Here science education indoctrinates student to have a deep faith their paradigm and method without unending questioning of the fundamentals. A position at variance with Popper. He states that a revolution occurs when “(1) a critical mass of anomalies has arisen and (2) a rival paradigm has appeared”(Godfrey-Smith, 2003) p. 82). However, critics have shown that it is possible for two different paradigms to co-exist at the time in the same field and that a revolution ould take place without the presence of a crisis.

My take home from Kuhn is the interesting articulation of the presence of science’s social structure and its role in shaping the science of knowledge creation.

Naturalists think that humans are biological organisms embedded in a physical world, and that they collect their knowledge about this world through their perceptual senses- eyes, ears, and so forth. Our perceptual senses are used to form judgments about objects in the world around us. Empiricists have worried that there will always be alternative theories of the world that are equally compatible with our observations. This gives rise to the problem of  "underdetermination".

 

For example, we may argue that musical tones such as middle C do not have existence as tones in the air; they appear, instead, to be qualities that the mind itself generates when the appropriate hair cells in the organ are stimulated. Nor does the color purple have existence as a quality in the world outside of the mind; there can be, in fact, no such thing as a beam of pure purple light inasmuch as purple is a unique kind of color that is perceived when vibrations at the opposite extremes of the visual spectrum (red and violet) are mixed together in the same beam. At least in this one case, the color seems created by the mind. But if this is so of purple, we may ask, is it not true, also, of all colors? Similarly, under certain circumstances heat is felt as cold and rotation as oscillation. It is not surprising, therefore, that philosophers have asked what properties an object might have in and of itself after due allowance has been made for those qualities that the mind and perspective of the observer have imposed upon it; nor is it surprising that they have asked what it would mean to insist on the objective existence of an object of which all of the qualities were mental. Realists, on the other hand, have held that there still remains a sense in which objects can have an existence that is independent of minds.

In Reynolds, we read about the purposes of research, which include: to classify; to predict or explain; and to give a sense of understanding. We also learned how best to couch theories to best give a sense of understanding—the causal form of theory.  Thus, a major objective of scientific inquiry is to explain phenomena, to answer the question ‘why?’ and not just the question ‘what?’ Godfrey-Smith (GS), in the chapter on explanation, confirms that science tells us why things happen. Unfortunately, there is no consensus among philosophers as to what constitutes a valid explanation.

Originally, the notion that science aims to explain why things happen was one that the empiricists refused to accept. When the logical positivists and logical empiricists finally conceded that science does truly explain phenomena, they came up with the covering law theory of explanation. But the concession, here, was only to logic—the explanations we give to phenomena must follow the rules of logic. The theory required explanations to have premises, which must contain at least one statement of a law of nature, and a conclusion, which is the event to be explained. GS noted the problems faced by this theory; particularly, the problem of asymmetry and its failure to make reference to causal relations.  But a theory based on causation, while compelling, begs the question of what the meaning of causation is. Even if the meaning is agreed upon, what kind of information about causes would constitute a good explanation? The unification theory holds that explanation is about unifying a diverse set of facts by subsuming them under some general principles. Such generalized principles could then be used to explain phenomena. This theory was advanced by people who were not comfortable with causal theory. But it too suffered from the asymmetry problem.

Given that both the causal and the unification theories have their separate shortcomings, GS suggests that some pluralism about explanation may be emerging—one that combines causation and unification. Even though this is a step in the right direction, GS prefers a theory on explanation based on contextualism. Simply put, what constitutes a good explanation may differ for different fields of science and may vary at different times within a given field of science.

GS contends that the twin problems of explanation and evidence (confirmation) should be handled together as there is often a close connection between the issues involved. The solution to an explanation problem may affect how we solve an evidence problem.

We earlier learned of the empiricists’ attempt at a theory of induction and confirmation and how they failed because of flaws in inductive logic as a basis for confirming scientific theories. In chapter 14, we learn that confirmation has been resuscitated, albeit with a new name, “evidence.” The new approach is Bayesianism, which is based on Bayes Theorem concepts of prior probability, likelihood, and posterior probability. Here, probability is interpreted to mean the degree of confidence placed on a claim.

The prior is the probability of the hypothesis before collecting any evidence. The posterior is the probability of the hypothesis in the light of the evidence that has been collected. The process of confirmation involves comparing the prior and posterior probabilities. To the extent that the posterior is higher than the prior, the evidence is adduced to be confirming the hypothesis.

The importance of good theory on evidence cannot be overemphasized. It is only when a theory has been confirmed can we confidently use it for prediction and explanation. Philosophers are hoping that Bayesianism will not fail them as induction did.

Rest of this book, chapter 10 – 15 gave me some insights philosophical thinking in the theory of science. For me this last part is interesting, because author gradually revealed his purpose and messages what he wants to give us. Finally author combined three ideas, Empiricism, Naturalism, and Scientific realism, to become one big idea to describe how science works as Kuhn’s assertion arguing that science cannot be described by any kind of simple empiricist formula, because science is a much more complicated machine than traditional empiricism ever imagined.

To reach this conclusion author explained about the Naturalism whose idea is that philosophy use result from the sciences to help answer philosophical questions and can do this even in the philosophy of science itself. And it discussed about the role of observation in science giving arguing example from Hanson, Kuhn, and Feyerabend. They insist that observation cannot function as an unbiased way of testing theories, because observational judgments are affected by the theoretical beliefs of the observer. However Empiricist still persist that even though there is a lot of complexity in the world, basic ideas of Empiricism capture the most fundamental features of how science works. Author also talked about reward scheme. One of interesting arguments is Strevens’ assertion arguing that payoff is given only if a research program solves the scientific problem, and the pie is shared unequally among those working on the successful program. He claimed that worker who joined early and made a big difference to the program’s chance of success get more than workers who joined late and made little difference.

Another major idea is scientific realism. A scientific realist thinks that science aims at describing the real structure of the world we live in. To establish scientific realism, author suggests that the best way to start is to ignore science for the moment and look first for a more general description of realist attitude. In addition author emphasized that we cannot get the right analysis by claiming that within all of science, a good explanation is something that satisfies either the causal test or the unification test. Also he mentioned that different fields have different concepts and standards of explanation bringing the idea of pluralism. I agree with his assertion indicating that scientific belief is not the product of us alone or of the world alone; it is the product of an interaction between our psychological capacities, our social organization, and the structure of the world.

Smith in the last part of his textbook gives the readers an overview for Naturalistic theory and its different versions. After reading the last chapters, it can be inferred that Naturalism holds that science is not a replacement for philosophy. However, there should be continuity between science and philosophy. On the other hand, naturalists didn’t settle on what this connection between science and philosophy should be like. Smith also emphasized on the difference between naturalism and Foundationalism. Naturalists think that foundation is not needed for the science. Moreover, Smith explains the similarities and differences among philosophers such as Hull, Merton, and Kitcher.

 

Hull stressed on the relationship between the scientist’s motivations and his personal goals, and also the motivations and science goals. Hull emphasized on the desire of scientists to get more accreditation through citing their work by others. Hull referred to this kind of recognition as the use. Merton also agreed with Hull in this point, however, Merton saw that recognition which is desired from scientists as being the first who come with ideas. Both Hull and Merton agreed on that what motivates scientists is recognition, but they had different definitions for recognition.

 

Kitcher tried to answer the question of “allocating scientists to different programs” that both Lakatos and laundan failed to answer, and which was considered as a gap in their views. Kitcher gave different reward approaches that make the best distribution of scientists to multiple programs. Though, he argued that the third reward approach gives good distribution. Scientists will have little incentives to join the promising program over time due to the fact that program will be crowded, and the pie will be shared equally on all scientists involved. As a result, the pie (prestige) will be shared among many scientists. Therefore, this reward system will force the scientists to consider and join other less promising programs while having a hope they may achieve success and their chances in obtaining high prestige are greater than their chances in the promising program. Stevens came up with other reward schema that is similar to kitcher’s third reward approach, but the idea in this schema was giving rewards to scientists in the successful program rewards as proportion of their contribution. From my point of view, I think both kitcher’s and Stevens’ reward approaches are almost the same. They have the same idea except the way they allocate the rewards.

Scientific change camps: one-process, two-process, in-between-process, or ‘whisker’ kind

One-process change camp believes that there is one unifying change resulting in outcomes within and outside a framework. This believe is generally based on general considerations rather than historic episodes of science. Popper views scientific change as a one-process of cycle of conjectures and refutations. He rejects the idea of framework as a myth constraining thought and knowledge. Quine’s holism proposition says knowledge of a part should be construed in the context of the whole and that there is no distinction between small and big or within and outside frameworks. There is no way to mark out this distinction. It is the same holistic tinkering of the web of belief. Thomas Ricketts supports Quine’s views.

Two-process change camp believes two distinguished changes  exist – one that take place within and another that take place outside the boundary of a framework. This believe draws from historical episodes. Kuhn views scientific change as two distinctive changes – change within a paradigm’s framework guided by the principles supplied by the framework (which could be construe as normal science) and change between paradigms unguided and problematic (which is regarded as revolutionary science). In support of Kuhn, Carnap argues that changes happen within and outside linguistic frameworks and they may be more than one. Lingustic frameworks define linguistic and logical rules guiding changes within frameworks. Scientists are at liberty to switch from one framework to the other depending on their utility. Others who support Kuhn’s views include Imre Lakatos, Larry Laudan, and Michael Friedman. Lakatos contributes the idea of research program which is a large-scale processes of scientific change. In contrast to Kuhn’s idea of mono-paradigm per field per time period, there may be more than one research program being performed in parallel and completion at the same time having a center hard core which never change. Larry Laudan extends the concept of research program to research tradition. A research tradition is loosely coupled by theories having a flux core. Within a tradition, ideas can freely migrate in and out of core.  Ideas can even be absorbed by another tradition. Scientists in a particular tradition respond to theories by accepting and treating it as true or pursuing and exploring it as a prospect.

In-between-process, or ‘whisker’ camp embraces philosophers that are neither in support of one- nor two-process change. These philosophers include Feyerabend, Peter Galison. Feyerabend for example argues that changes can happen in frameworks but are not restricted or constrained by its walls. In contrast to Kuhn simultaneous change in theories, methods, and data, Galison explains that fundamental changes in experimental tradition do not happen simultaneously. Rather, disruptive changes can still be managed in an orderly manner because theories, methods, and data do not change all at the same time.

Strong program. Having mentioned the major camps as identified from historical episode, I will move on to discuss some key movements. Barry Barnes and David Bloor facilitated an interdisciplinary group that birthed ‘strong program’. Strong program is an interest-driven in contract with data-driven empiricism. It defines a symmetry and political connection of science. Symmetry principle says all forms of beliefs and behavior should be approached using the same kinds of norms of argumentation and justification created and maintain by human interaction. Political connection of science takes scientific theories and interprets them in relation to the social circumstances. Meaning a scientific idea is favored if it benefits a particular social group. This is problematic. However, other methods of reward systems were propounded.

Reward systems in science. David Hull’s theory of science states that science runs a combination of cooperating and competing activities where recognition is the basic reward. Hull & Merton argue that scientists operate in a special context in that they inherit the ideas and methods of their predecessors. They seek utility of their work through replicability process. This outlook is seen in the Royal Society of London’s allocation of credit by publishing proceedings. Kitcher’s division of scientific labor posit that individual efforts can be rewards among competing research program by sharing a reward pie based on number of members and likelihood of success. Michael Stevens extended the pie rewarding system by advocating for rewards that are in proportion to the contribution of a particular researcher. The feminists argue the individualistic and competitive posture of mainstream science. They maintain that science will benefit from the diversity that women style of thought and investigation if reward system has been conducted in a less competitive atmosphere. Hull’s argument suggests a subtle tradeoff of feminist position for the benefit of balance of competition and cooperation. Peers’ recognition is another form of reward system. It has been noted that  big financial rewards have being received from external sources. Kuhn however warns of the danger of science be susceptible to the pushes and pulls of external political and economic life.

Here is the question: how can science community maintain its sanity and ‘objectivity’ in the face of temptations from external reward sources?

Scientific Realism. A realist thinks we inhabit a common reality regardless of our different views. This reality is comprised of and dependent n thoughts, theories, and other symbols. Godfree-Smith argues that science embraces common reality and that it aims at giving accurate descriptions or presentations of what reality is like both observable and unobservable. Godfree-Smith identifies Kuhn and Latour as metaphysical constructivists in that they view that the world is created or constructed by scientific theorizing. These views are far from reality. Thus, representation in science needs special attention. Science tries to provide an accurate representation of the world using systems of linguistic or model entities. Entities are concretized when they are labeled using language. Linguistic entities in form of hypotheses present a form of representation. A model is a structure that represents another structure by virtue of an abstract similarity relationship between them. Thus, science engages both linguistic and model entities in representing phenomena.

Bayesianism. I take a step back to the fundamental question of testing, confirmation, and evidence. Bayesians have used the theory of probability to understand the power of evidence.  Under Bayesianism, claims or hypotheses are assigned a priori probability depicting the power of their evidence. These initial probabilities are updated as updated as evidence becomes available. The freedom in choosing the initial probability values which is construe as the Bayesianism strength is also viewed as the weakness. Though Bayesians argues that the effects of the freely chosen probability will be watched off at convergence as real actual data becomes available. But this is not convincing to Bayesian’s critics.

In the light of these philosophies, paradigms, frameworks, strategies, traditions, rewards, and history, science endeavors are complex. No one size fits all when striving to understand phenomena and create knowledge. I have been internalizing useful strategies of investigating and creating knowledge obtained from the book, blog, and class discussion. In particular, I reckon with the roles of linguistic and model representation of science. I also sympathize with most of the realist viewpoints. I believe we should be flexible when responding to phenomena by selecting appropriate philosophical orientation and representation.