Epistemic Disjunctivism: A New Story about a Familiar Picture
Bibliographic record
Abstract
The primary aim of this thesis is to evaluate the position known as ‘epistemic disjunctivism’ and its relation to the traditional epistemological framework it is meant to replace. In particular, this will involve the investigation of the points at which John McDowell takes disjunctivism to conflict with traditional empiricist approaches to epistemology with an eye to whether or not any substantive conflicts necessarily arise between these two positions. In the course of this thesis, I shall explain the disjunctivist position, directly address McDowell’s arguments to the effect that traditional empiricism is in need of replacement by disjunctivism, and then draw my conclusions in light of the earlier discussion. In the course of arguing against the traditional approach, McDowell considers two positions which he takes to characterize empiricist accounts of epistemology. He calls these positions the ‘highest common factor’ conception and the ‘hybrid’ view, and argues that they are not only insufficient for a satisfying epistemological account, but undermine the possibility of any account constructed upon them to be satisfactory. These positions are both fundamentally motivated by familiar sceptical arguments and seem to lie at the heart of empiricism. As we shall see, however, should both of these characteristically empiricist positions be interpreted charitably, they need not conflict with disjunctivism, or at least not as McDowell defines it. Indeed, it would seem that the only direct conflicts between the two accounts in question are ultimately semantic, and that the epistemic stories told by the empiricist and the disjunctivist are fundamentally compatible.
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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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.011 | 0.040 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".