(Almost) All about error
BOOK REVIEW Metascience (2012) 21:709–713 DOI 10.1007/s11016-011-9618-1
Deborah G. Mayo and Aris Spanos (eds): Error and inference: Recent exchanges on experimental reasoning, reliability, objectivity, and rationality. New York: Cambridge University Press, 2010, xvii+419 pp
The ERROR’06 (experimental reasoning, reliability, objectivity, and rationality) conference held at Virginia Tech aimed to advance the discussion of some central themes in philosophy of science debated by Deborah Mayo and her more-or-less friendly critics over the years. The volume here reviewed brings together the contributions of these critics and Mayo’s responses to them (with Mayo’s collaborator Aris Spanos). (I helped with the organization of the conference and, with Mayo and Jean Miller, edited a separate collection of workshop papers that were presented there, published as a special issue of Synthese.) My review will focus on a couple of themes I hope to be of interest to a broad philosophical audience, then turn more brieﬂy to an overview of the entire collection. The discussions in Error and Inference (E&I) are indispensable for understanding several current issues regarding the methodology of science.
The remarkably useful introductory chapter lays out the broad themes of the volume and discusses ‘‘The Error-Statistical Philosophy’’. Here, Mayo and Spanos provide the most succinct and non-technical account of the error-statistical approach that has yet been published, a feature that alone should commend this text to anyone who has found it difﬁcult to locate a reading on error statistics suitable for use in teaching.
Mayo holds that the central question for a theory of evidence is not the degree to which some observation E conﬁrms some hypothesis H but how well-probed for error a hypothesis H is by a testing procedure T that results in data x0. This reorientation has far-reaching consequences for Mayo’s approach to philosophy of science. On this approach, addressing the question of when data ‘‘provide good evidence for or a good test of’’ a hypothesis requires attention to characteristics of the process by means of which the data are used to bear on the hypothesis. Mayo identiﬁes the starting point from which her account is developed as the ‘‘Weak Severity Principle’’ (WSP):
Data x0 do not provide good evidence for hypothesis H if x0 results from a test procedure with a very low probability or capacity of having uncovered the falsity of H (even if H is incorrect). (21)
The weak severity principle is then developed into the full severity principle (SP), according to which ‘‘data x0 provide a good indication of or evidence for hypothesis H (just) to the extent that test T has severely passed H with x0’’ where H passes a severe test T with x0 if x0 ‘‘agrees with’’ H and ‘‘with very high probability, test T would have produced a result that accords less well with H than doesx0, if H were false or incorrect’’ (22). This principle constitutes the heart of the error-statistical account of evidence, and E&I, by including some of the most important critiques of the principle, provides a forum in which Mayo and Spanos attempt to correct misunderstandings of the principle and to clarify its meaning and application.
The appearance in the WSP of the disjunctive phrase ‘‘a very low probability or capacity’’ (my emphasis) indicates a point central to much of this clariﬁcatory work. The error-statistical account is resolutely frequentist in its construal of probability. It is commonly held (including by some frequentists) that the rationale for frequentist statistical methods lies exclusively in the fact that they can sometimes be shown to have low error rates in the long run. Throughout E&I, Mayo insists that this ‘‘behaviorist rationale’’ is not applicable when it comes to evaluating a particular body of data in order to determine what inferences may be warranted. That evaluation rests upon thinking about the particular data and the inference at hand in light of the capacity of the test to reveal potential errors in the inference drawn. Frequentist probabilities are part of how one models the error-detecting capacities of the process. As Mayo explains in a later chapter co-authored with David Cox, tests of hypotheses function analogously to measuring instruments: ‘‘Just as with the use of measuring instruments, applied to a speciﬁc case, we employ the performance features to make inferences about aspects of the particular thing that is measured, aspects that the measuring tool is appropriately capable of revealing’’ (257).
One of the most fascinating exchanges in E&I concerns the role of severe testing in the appraisal of ‘‘large-scale’’ theories. According to Mayo, theory appraisal proceeds by a ‘‘piecemeal’’ process of severe probing for speciﬁc ways in which a theory might be in error. She illustrates this with the history of experimental tests of theories of gravity, emphasizing Clifford Will’s parametrized post-Newtonian (PPN) framework, by means of which all metric theories of gravity can be represented in their weak-ﬁeld, slow-motion limits by means of ten parameters. Experimental work on gravity theories then severely tests hypotheses about the values of those parameters. Rather than attempting to conﬁrm or probabilify the general theory of relativity (GTR), the aim is to learn about the ways in which GTR might be in error, more generally to ‘‘measure how far off what a given theory says about a phenomenon can be from what a ‘correct’ theory would need to say about it’’ (55).
Alan Chalmers and Alan Musgrave both challenge this view. According to Chalmers, no general theory, whether ‘‘low level’’ or ‘‘high level’’, can pass a severe test because the content of theories surpasses whatever empirical evidence supports them. As a consequence, Chalmers argues, Mayo’s severe-testing account of scientiﬁc inference must be incomplete because even low-level experimental testing sometimes demands relying on general theoretical claims. Similarly, Musgrave accuses Mayo of holding that (general) theories are not tested by ‘‘testing their consequences’’, but that ‘‘all that we really test are the consequences’’ (105), leaving her with ‘‘nothing to say’’ about the assessment, adoption, or rejection of general theories (106). Continue reading
Monthly Archives: May 2013
Today is (statistician) Allan Birnbaum’s birthday. He lived to be only 53 [i]. From the perspective of philosophy of statistics and philosophy of science, Birnbaum is best known for his work on likelihood, the Likelihood Principle [ii], and for his attempts to blend concepts of likelihood with error probability ideas to obtain what he called “concepts of statistical evidence”. Failing to find adequate concepts of statistical evidence, Birnbaum called for joining the work of “interested statisticians, scientific workers and philosophers and historians of science”–an idea I would heartily endorse! While known for attempts to argue that the (strong) Likelihood Principle followed from sufficiency and conditionality principles, a few years after publishing this result, he seems to have turned away from it, perhaps discovering gaps in his argument.
NATURE VOL. 225 MARCH 14, 1970 (1033)
LETTERS TO THE EDITOR
Statistical methods in Scientific Inference
It is regrettable that Edwards’s interesting article, supporting the likelihood and prior likelihood concepts, did not point out the specific criticisms of likelihood (and Bayesian) concepts that seem to dissuade most theoretical and applied statisticians from adopting them. As one whom Edwards particularly credits with having ‘analysed in depth…some attractive properties” of the likelihood concept, I must point out that I am not now among the ‘modern exponents” of the likelihood concept. Further, after suggesting that the notion of prior likelihood was plausible as an extension or analogue of the usual likelihood concept (ref.2, p. 200), I have pursued the matter through further consideration and rejection of both the likelihood concept and various proposed formalizations of prior information and opinion (including prior likelihood). I regret not having expressed my developing views in any formal publication between 1962 and late 1969 (just after ref. 1 appeared). My present views have now, however, been published in an expository but critical article (ref. 3, see also ref. 4) , and so my comments here will be restricted to several specific points that Edwards raised.
If there has been ‘one rock in a shifting scene’ or general statistical thinking and practice in recent decades, it has not been the likelihood concept, as Edwards suggests, but rather the concept by which confidence limits and hypothesis tests are usually interpreted, which we may call the confidence concept of statistical evidence. This concept is not part of the Neyman-Pearson theory of tests and confidence region estimation, which denies any role to concepts of statistical evidence, as Neyman consistently insists. The confidence concept takes from the Neyman-Pearson approach techniques for systematically appraising and bounding the probabilities (under respective hypotheses) of seriously misleading interpretations of data. (The absence of a comparable property in the likelihood and Bayesian approaches is widely regarded as a decisive inadequacy.) The confidence concept also incorporates important but limited aspects of the likelihood concept: the sufficiency concept, expressed in the general refusal to use randomized tests and confidence limits when they are recommended by the Neyman-Pearson approach; and some applications of the conditionality concept. It is remarkable that this concept, an incompletely formalized synthesis of ingredients borrowed from mutually incompatible theoretical approaches, is evidently useful continuously in much critically informed statistical thinking and practice [emphasis mine].
While inferences of many sorts are evident everywhere in scientific work, the existence of precise, general and accurate schemas of scientific inference remains a problem. Mendelian examples like those of Edwards and my 1969 paper seem particularly appropriate as case-study material for clarifying issues and facilitating effective communication among interested statisticians, scientific workers and philosophers and historians of science.
New York University
Courant Institute of Mathematical Sciences,
251 Mercer Street,
New York, NY 10012
Birnbaum’s confidence concept, sometimes written (Conf), was his attempt to find in error statistical ideas a concept of statistical evidence–a term that he invented and popularized. In Birnbaum 1977 (24), he states it as follows:
(Conf): A concept of statistical evidence is not plausible unless it finds ‘strong evidence for J as against H with small probability (α) when H is true, and with much larger probability (1 – β) when J is true.
Birnbaum questioned whether Neyman-Pearson methods had “concepts of evidence” simply because Neyman talked of “inductive behavior” and Wald and others cauched statistical methods in decision-theoretic terms. I have been urging that we consider instead how the tools may actually be used, and not be restricted by the statistical philosophies of founders (not to mention that so many of their statements are tied up with personality disputes, and problems of “anger management”). Recall, as well, E. Pearson’s insistence on an evidential construal of N-P methods, and the fact that Neyman, in practice, spoke of drawing inferences and reaching conclusions (e.g., Neyman’s nursery posts, links in [iii] below). Continue reading
Since posting on the High Quality Research act a few weeks ago, I’ve been following it in the news, have received letters from professional committees (asking us to write letters), and now see that Nathan A. Schachtman, Esq., PC posted the following on May 25, 2013 on his legal blog*:
“The High Quality Research Act” (HQRA), which has not been formally introduced in Congress, continues to draw attention. See“Clowns to the left of me, Jokers to the right.” Last week, Sarewitz suggests that “the problem” is the hype about the benefits of pure research and the let down that results from the realization that scientific progress is “often halting and incremental,” with much research not “particularly innovative or valuable.” Fair enough, but why is this Congress such an unsophisticated consumer of scientific research in the 21st century? How can it be a surprise that the scientific community engages in the same rent-seeking behaviors as do other segments of our society? Has it escaped Congress’s attention that scientists are subject to enthusiasms and group think, just like, … congressmen?
Still, Sarewitz believes that the HQRA bill is not particularly threatening to the funding of science:
“In other words, it’s not a very good bill, but neither is it much of a threat. In fact, it’s just the latest skirmish in a long-running battle for political control over publicly funded science — one fought since at least 1947, when President Truman vetoed the first bill to create the NSF because it didn’t include strong enough lines of political accountability.”
This sanguine evaluation misses the effect of the superlatives in the criteria for National Science Foundation funding:
“(1) is in the interests of the United States to advance the national health, prosperity, or welfare, and to secure the national defense by promoting the progress of science;
(2) is the finest quality, is ground breaking, and answers questions or solves problems that are of utmost importance to society at large; and
(3) is not duplicative of other research projects being funded by the Foundation or other Federal science agencies.” Continue reading
Andrew Gelman had said he would go back to explain why he sided with Neyman over Fisher in relation to a big, famous argument discussed on my Feb. 16, 2013 post: “Fisher and Neyman after anger management?”, and I just received an e-mail from Andrew saying that he has done so: “In which I side with Neyman over Fisher”. (I’m not sure what Senn’s reply might be.) Here it is:
“In which I side with Neyman over Fisher” Posted by Andrew on 24 May 2013, 9:28 am
As a data analyst and a scientist, Fisher > Neyman, no question. But as a theorist, Fisher came up with ideas that worked just fine in his applications but can fall apart when people try to apply them too generally.
Here’s an example that recently came up.
Deborah Mayo pointed me to a comment by Stephen Senn on the so-called Fisher and Neyman null hypotheses. In an experiment with n participants (or, as we used to say, subjects or experimental units), the Fisher null hypothesis is that the treatment effect is exactly 0 for every one of the n units, while the Neyman null hypothesis is that the individual treatment effects can be negative or positive but have an average of zero.
Senn explains why Neyman’s hypothesis in general makes no sense—the short story is that Fisher’s hypothesis seems relevant in some problems (sometimes we really are studying effects that are zero or close enough for all practical purposes), whereas Neyman’s hypothesis just seems weird (it’s implausible that a bunch of nonzero effects would exactly cancel). And I remember a similar discussion as a student, many years ago, when Rubin talked about that silly Neyman null hypothesis. Continue reading
Our presentation falls under the second of the bulleted questions for the conference (conference blog is here):
How do methods of data generation, statistical modeling, and inference influence the construction and appraisal of theories?
Statistical methodology can influence what we think we’re finding out about the world, in the most problematic ways, traced to such facts as:
- All statistical models are false
- Statistical significance is not substantive significance
- Statistical association is not causation
- No evidence against a statistical null hypothesis is not evidence the null is true
- If you torture the data enough they will confess.
(or just omit unfavorable data)
These points are ancient (lying with statistics, lies damn lies, and statistics)
People are discussing these problems more than ever (big data), but it’s rarely realized is how much certain methodologies are at the root of the current problems.
All Statistical Models are False
Take the popular slogan in statistics and elsewhere is “all statistical models are false!”
What the “all models are false” charge boils down to:
(1) the statistical model of the data is at most an idealized and partial representation of the actual data generating source.
(2) a statistical inference is at most an idealized and partial answer to a substantive theory or question.
- But we already know our models are idealizations: that’s what makes them models
- Reasserting these facts is not informative,.
- Yet they are taken to have various (dire) implications about the nature and limits of statistical methodology
- Neither of these facts precludes the use of these to find out true things
- On the contrary, it would be impossible to learn about the world if we did not deliberately falsify and simplify.
- Notably, the “all models are false” slogan is followed up by “But some are useful”,
- Their usefulness, we claim, is being capable of adequately capturing an aspect of a phenomenon of interest
- Then a hypothesis asserting its adequacy (or inadequacy) is capable of being true!
Note: All methods of statistical inferences rest on statistical models.
What differentiates accounts is how well they step up to the plate in checking adequacy, learning despite violations of statistical assumptions (robustness)
Statistical significance is not substantive significance
Statistical models (as they arise in the methodology of statistical inference) live somewhere between
- Substantive questions, hypotheses, theories H
- Statistical models of phenomenon, experiments, data: M
- Data x
What statistical inference has to do is afford adequate link-ups (reporting precision, accuracy, reliability)
__________________4__________________ Continue reading
Writing a blog like this, a strange and often puzzling exercise, does offer a forum for sharing half-baked chicken-scratchings from the back of frayed pages on themes from our Onto-Meth conference from two weeks ago. (The previous post had notes from blogger and attendee, Gandenberger.)
Several of the talks reflect a push-back against the idea that the determination of “ontology” in science—e.g., the objects and processes of theories, models and hypotheses—is (or should strive to correspond to?) “real” objects in the world and/or what is approximately the case about them. Instead, at least some of the speakers wish to liberate ontology to recognize how “merely” pragmatic goals, needs, and desires are not just second-class citizens, but can and do (and should?) determine the categories of reality. Well there are a dozen equivocations here, most of which we did not really discuss at the conference.
In my own half of the Spanos-Mayo (D & P presentation) I granted and even promoted the idea of a methodology that was pragmatic while also objective, so I’m not objecting to that part. The measurement of my weight is a product of “discretionary” judgments (e.g., to weigh in pounds with a scale having a given precision), but it is also a product of how much I really weigh (no getting around it). By understanding the properties of methodological tools and measuring systems, it is possible to “subtract out” the influence of the judgments to get at what is actually the case. At least approximately. But that view is different, it seems to me, from someone like Larry Laudan (at least in his later metamorphosis). Even though he considers his “reticulated” view a fairly hard-nosed spin on the Kuhnian idea of scientific paradigms as invariably containing an ontology (e.g., theories), a methodology, and (what he called) an “axiology” or set of aims (OMA), Laudan seems to think standards are so variable that what counts as evidence is constantly fluctuating (aside from maybe retaining the goal of fitting diverse facts). So I wonder if these pragmatic leanings are more like Laudan or more like me (and my view here, I take it, is essentially that of Peirce). I am perfectly sympathetic to the piecemeal “locavoracity” idea in Ruesche, by the way.
My worry, one of them, is that all kinds of rival entities and processes arise to account for (accord with, predict, and purportedly explain) data and patterns in data, and don’t we need ways to discriminate them? During the open discussion, I mentioned several examples, some of which I can make out all scrunched up in the corners of my coffee-logged program, such as appeals to “cultural theories” of risk and risk perceptions. These theories say appeals to supposedly “real” hazards, e.g, chance of disease, death, catastrophe, and other “objective” risk assessments are wrong. They say it is not only possible but preferable (truer?) to capture attitudes toward risks, e.g., GM foods, nuclear energy, climate change, breast implants, etc. by means of one or another favorite politico-cultural grid-group categories (e.g., marginal-individualists, passive-egalitarians, hierarchical-border people, fatalists, etc.). (Your objections to these vague category schemes are often taken as further evidence that you belong in one of the pigeon-holes!) And the other day I heard a behavioral economist declare that he had found the “mechanism” to explain deciding between options in virtually all walks of life using a regression parameter, he called it beta, and guess what? beta = 1/3! He proved it worked statistically too. He might be right, he had a lot of data. Anyway, in my deliberate attempt to trigger discussion at the conference end, I was wondering if some of the speakers and/or attendees (Danks, Woodward, Glymour? Anyone?) had anything to say about cases that some of us might wish to call reification. Continue reading
Ph.D graduate student: Dept. of History and Philosophy of Science & Dept. of Statistics
University of Pittsburgh
Some Thoughts on the O&M 2013 Conference
I was struck by how little speakers at the Ontology and Methodology conference engaged with the realism/antirealism debate. Laura Ruetsche defended a version of Arthur Fine’s Natural Ontological Attitude (NOA) in the first talk of the conference, but none of the speakers after her addressed the debate directly. David Danks and Jim Woodward made it particularly clear that they were deliberately avoiding questions about realism in favor of questions about what kinds of ontologies our theories should have in order to best serve the various purposes for which we develop them.
I am not criticizing the speakers! I am inclined to agree with Clark Glymour that the kinds of questions Danks and Woodward addressed are more interesting and important than questions about “what’s really real.” On the other hand, I worry that we lose something when we focus only on the use of science toward such ends as prediction and control. During the discussion period at the end of the conference, Peter Godfrey-Smith argued that science has some value simply for telling us what really is the case. For instance, science tells us that all living things on earth have a common ancestor, and that fact is a good thing to know regardless of whether or not it helps us predict or control anything.
One feature of the realism/antirealism debate that has long bothered me is that it treats all of “our best sciences” as if they had roughly the same epistemic status. In fact, realism about quantum field theory, for instance, is much harder to defend than realism about evolutionary biology. I am inclined to dismiss the realism debate as ill-formed insofar as it presumes that the question of scientific realism is a single question that spans all of the sciences. I am also suspicious of the debate in its bread-and-butter domain of fundamental physics. It is not clear to me that there is such a thing as fundamental physics; that if there is such a thing as fundamental physics, then it is converging toward a unified ontology; that if it is converging toward a unified ontology, then we can make sense of the question whether or not that ontology is correct; or that if we can make sense of the question whether or not that ontology is correct, then we have the means to give a justified answer to that question.
Nevertheless, as Glymour pointed out during the open discussion period, there are still good and open questions to address about whether and how we are justified in believing that science tells us the truth in other domains (such as evolutionary theory) where the realism question seems relatively well-formed and answerable. We can dismiss questions about “what’s really real” at a “fundamental level” while still thinking that philosophers of science should have a story to tell the 46% of Americans who believe that human beings were created in more or less their current form within the last 10,000 years—not a story about how science serves purposes of prediction and control, but a story about how science can help us find the truth.
“A sense of security regarding the future of statistical science…” Anon review of Error and Inference
Aris Spanos, my colleague and co-author (Economics),recently came across this seemingly anonymous review of our Error and Inference (2010) [E & I]. It’s interesting that the reviewer remarks that “The book gives a sense of security regarding the future of statistical science and its importance in many walks of life.” I wish I knew just what the reviewer means–but it’s appreciated regardless.
2010 American Statistical Association and the American Society for Quality
TECHNOMETRICS, AUGUST 2010, VOL. 52, NO. 3, Book Reviews, 52:3, pp. 362-370.
Error and Inference: Recent Exchanges on Experimental Reasoning, Reliability, and the Objectivity and Rationality of Science, edited by Deborah G. MAYO and Aris SPANOS, New York: Cambridge University Press, 2010, ISBN 978-0-521-88008-4, xvii+419 pp., $60.00.
This edited volume contemplates the interests of both scientists and philosophers regarding gathering reliable information about the problem/question at hand in the presence of error, uncertainty, and with limited data information.
The volume makes a signiﬁcant contribution in bridging the gap between scientiﬁc practice and the philosophy of science. The main contribution of this volume pertains to issues of error and inference, and showcases intriguing discussions on statistical testing and providing alternative strategy to Bayesian inference. In words, it provides cumulative information towards the philosophical and methodological issues of scientiﬁc inquiry at large.
The target audience of this volume is quite general and open to a broad readership. With some reasonable knowledge of probability theory and statistical science, one can get the maximum beneﬁt from most of the chapters of the volume. The volume contains original and fascinating articles by eminent scholars (nine, including the editors) who range from names in statistical science to philosophy, including D. R. Cox, a name well known to statisticians.
The editors have done a superb job in presenting, organizing, and structuring the material in a logical order. The “Introduction and Background” is nicely presented and summarized, allowing for a smooth reading of the rest of the volume. There is a broad range of carefully selected topics from various related ﬁelds reﬂecting recent developments in these areas. The rest of the volume is divided in nine chapters/sections as follows:
1. Learning from Error, Severe Testing, and the Growth of Theoretical
2. The Life of Theory in the New Experimentalism
3. Revisiting Critical Rationalism
4. Theory Conﬁrmation and Novel Evidence
5. Induction and Severe Testing
6. Theory Testing in Economics and the Error-Statistical Perspective
7. New Perspectives on (Some Old) Problems of Frequentist Statistics
8. Casual Modeling, Explanation and Severe Testing
9. Error and Legal Epistemology
In summary, this volume contains a wealth of knowledge and fascinating debates on a host of important and controversial topics equally important to the philosophy of science and scientiﬁc practice. This is a must-read—I enjoyed reading it and I am sure you will too! The book gives a sense of security regarding the future of statistical science and its importance in many walks of life. I also want to take the opportunity to suggest another seemingly related book by Harman and Kulkarni (2007). The review of this book was appeared in Technometricsin May 2008 (Ahmed 2008).
The following are chapters in E & I (2010) written by Mayo and/or Spanos, if you’re interested. If you produce a palindrome meeting the extremely simple requirements for May (by May 25 or so), you can win a free copy! Continue reading
See new rejected post.(You may comment here or on the Rejected Posts blog)
April 18, 2013
TO [BE SUPPLIED]
Be it enacted by the Senate and House of Representatives of the United States of America in Congress assembled,
SECTION 1. SHORT TITLE.
This act may be cited as the “High Quality Research Act”.
SECTION 2. HIGH QUALITY RESEARCH.
(a) CERTIFICATION.—prior to making an award of any contract or grant funding for a scientific research project, the Director of the NSF shall publish a statement on the public website of the Foundation that certifies that the research project—
(1) is in the interests of the U.S. to advance the national health, prosperity, or welfare, and to secure the national defense by promoting the progress of science;
(2) is the finest quality, is ground breaking, and answers questions or solves problems that are of utmost importance to society at large; and
(3) is not duplicative of other research projects being funded by the Foundation or other Federal Science agencies.
(b) TRANSFER OF FUNDS.—Any unobligated funds for projects ot meeting the requirements of subjection (a) may be awarded to other scientific research projects that do meet such requirements.
(e) INITIAL IMPLEMENTATION REPORT.—Not later than 60 days after the date of enactment of this Act, the Director shall report to the Committee on Commerce, Science, and Transportation of the Senate and the Committee on Science, Space, and Technology of the House of Representatives on how the requirements set for in subsection (a) are being implemented.
(d) NATIONAL SCIENCE BOARD IMPLEMENTATION REPORT. __ Not later than 1 year after the date of enactment of this act, the national science board shall report to the committee on commerce, science, and transportation of the senate and the committee on science, space and technology of the house of representatives its findings and recommendations on how the requirements of subsection (a) are being implemented.
Link to the Bill:
Rep. Lamar Smith,author of the Bill, listed five NSF projects about which he has requested further information.
1. Award Abstract #1247824: “Picturing Animals in National Geographic, 1888-2008,” March 15, 2013, ($227,437);
2. Award Abstract #1230911: “Comparative Histories of Scientific Conservation: Nature, Science, and Society in Patagonian and Amazonian South America,” September 1, 2012 ($195,761);
3. Award Abstract #1230365: “The International Criminal Court and the Pursuit of Justice,” August 15, 2012 ($260,001);
4. Award Abstract #1226483, “Comparative Network Analysis: Mapping Global Social Interactions,” August 15, 2012, ($435,000); and
5. Award Abstract #1157551: “Regulating Accountability and Transparency in China’s Dairy Industry,” June 1, 2012 ($152,464).
MAY 9, 2013
On page 1 of the New York Times yesterday was an article, “The Last Refuge From Scandal? Professorships”:
The traditional path to an academic job is long and laborious: the solitude and penury of graduate study, the scramble for one of the few open positions in each field, the blood sport of competitive publishing. But while colleges have always courted accomplished public figures, a leap to the front of the class has now become a natural move for those who have suffered spectacular career flameouts. At this point, the transition from public disgrace to college lectern is so familiar that when Mr. Galliano merely stepped foot on the campus of Central Saint Martins, an art and design school in London, speculation rippled around the world— incorrectly — that he would soon be teaching there.
I guess this shouldn’t surprise anyone. Sexy course titles and “novelty academics” are pretty old-hat; power and scandal, even if on the sleazy side, attract students; and if students are buying, universities can’t be blamed for selling. Or can they? Here are some examples they cite:
More recently, Parsons the New School for Design announced that John Galliano, the celebrated clothing designer who lost his job at Christian Dior after unleashing a torrent of anti-Semitic vitriol in a bar, would be leading a four-day workshop and discussion called “Show Me Emotion.”
And David H. Petraeus, the general turned intelligence chief turned ribald punch line, will have not one college paycheck, but two. Last month, the City University of New York said he would be the next visiting professor of public policy at Macaulay Honors College. On Thursday, the University of Southern California announced that Mr. Petraeus would also be teaching there…
Despite a petition objecting to Galliano, there seems to be little public concern that offering such courses threatens a university’s ethical standards, especially, perhaps, if “only” sexual transgressions are involved. Still, while I can see students wanting to enroll in a course taught by a Petreaus or a Spitzer, I doubt the same would be true for one run by a Deiderick Stapel*. Is it because in the former cases the scandal does not directly touch on their accomplishments? Is there a justifiable principle of distinction operating?** (Or might it depend on the course?) Continue reading
May 4 (Saturday):
8:30-9:00: Pastries & Coffee (Continental Breakfast) outside of Pamplin 2030
9:15-10:00 Ruetsche: “Method, Metaphysics, and Quantum Theory”
10:25-10:40 coffee break
10:40-11:05 Shech, “Phase Transitions, Ontology and Earman’s Sound Principle”
11:20-12:05 Godfrey-Smith, “Evolution and Agency: A Case Study in Ontology and Methodology”
12:30-1:30 Box Lunch
AFTERNOON SESSIONS: Continue reading