Fundamental Problems of Epistemology, Symposium in Honour of Brian Grant
Bibliographic record
Abstract
Mark Migotti, Fallibilism, Skepticism, Pragmatism Fallibilism emerged in and from Peirce’s campaign against “the spirit of Cartesianism”, and specifically his conviction that “[p]hilosophy ought to imitate the successful sciences in its methods, … and to trust rather to the multitude and variety of its arguments than to the conclusiveness of any one” (5.265). In respect of epistemology—which took shape as a sub-discipline of philosophy in the century or so since Peirce began to articulate the ideas he would later “collect … under the designation fallibilism” (1. 13)—the promise of fallibilism is the prospect of habitable ground in between theories that make ambitious claims to have discovered indubitable foundations for knowledge on the one side, and skeptical rebuttals of such theories on the other. In this paper I will explore the relations of fallibilism to skepticism with an eye to identifying the relations of both with the pragmatist “reconstruction” of epistemology undertaken by Susan Haack. Ron Wilburn, Epistemic Contextualism and the Concerns of the Skeptic Ignoring minor endogenous disagreements, we can take epistemic contextualism (EC) to be the thesis that the standards which must be met by a knowledge claimant vary with (especially conversational) contexts of utterance. Thus, even though in some contexts knowledge claims must satisfy relatively low epistemic contexts, in other contexts these very same claims must satisfy much higher standards as more and more remote possible sources of disinformation and error (ultimately generating skeptical scenarios) are raised for consideration. Thus construed, contextualism is a semantic thesis which may or may not be taken to have epistemological consequences: it can concern only knowledge claims, or it can concern the knowledge relation itself. Let’s call the view that what “knowledge” means depends on contextual factors “Semantic EC.” Let’s call the view that what knowledge is depends on contextual factors “Substantive EC.” Finally, let’s call the claim that Semantic EC implies Substantive EC the “Implication Thesis.” Thus, Semantic EC may or may or may not be seen as related to Substantive EC as a function of whether one accepts the “Implication Thesis.” In this paper, I argue for the Implication Thesis. Note that that in doing this I am not arguing for Substantive EC; I am merely arguing that Semantic EC’s plausibility presupposes Substantive EC. Most recent contextualists maintain that “knows” is merely one more contextual term amongst others, which suggests that Semantic EC receives support from comparisons with other ordinary language indexicals. On this account, the semantic and epistemological enterprises are as apples are to oranges. Just as the truth conditions of a tokening of the sentence “I am hungry” depend on contextual features concerning the identity of the speaker, the truth conditions of a tokening of the sentence “S knows that p” depends upon S’s context of utterance. But just as the mere token reflexiveness of “I” need tell us nothing about the nature of the self, the contextuality of “knows” need tell us nothing about the nature of knowledge. I argue against this claim on the grounds that semantic analysis should generally be buttressed by metaphysical presuppositions, and that appropriate metaphysical presuppositions suggest that Semantic EC is, at the very least, highly questionable, a fact which becomes apparent when we contrast “knows” with other indexical terms with which it is often compared. The consequences of this for skepticism are significant. Semantic EC, along with its response to skepticism, presupposes Substantive EC. However, Substantive EC requires an argument which Semantic EC doesn’t provide. Michael Williams presents the best arguments for Substantive EC in his book Unnatural Doubts. I conclude with a general critique of these arguments. Jeremy Fantl, On Engaging With Arguments You Know Are Misleading Some people offer counterarguments in the public sphere to positions you know are false. Sometimes it might be difficult to figure out what’s wrong with those counterarguments, even if you continue to know that their conclusions are false. Given that you know they are misleading, you shouldn’t be willing to reduce your confidence in your position in response to the argument, even if you can’t figure out what’s wrong with them; you should be ‘closed-minded’ toward them. Even so, it can be tempting to engage with such counterarguments. But in many standard situations closed-minded engagement requires either of two risky choices. Either you honestly represent your attitudes (as in the activist strategy known as “Nonviolent Communication”), in which case you run the risk of being ineffective. Or you risk being a “concern troll”: you fail to honestly represent yourself as closed-minded (as in the activist strategy employed by the “Listening Project”). In that case you run the risk of problematically exploiting misconceptions your interlocutors have about you in order to get them to change their attitudes. In many situations, this precludes the permissibility of closed-minded engagement and, given that you know that the counterarguments are misleading, engagement at all.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".