Bibliographic record
Abstract
This paper recapitulates my four primary lines of argument that what is wrong with scientific realism is not realist answers to questions to which various anti-realists give different answers, but instead assumptions shared by realists and anti-realists in framing the question. Each strategy incorporates its predecessors as a consequence. A first, minimalist challenge, taken over from Arthur Fine and Michael Williams, rejects the assumption that the sciences have a general aim or goal. A second consideration is that realists and antirealists undertake a mistaken, substantive commitment to a separation between mind and world, which allows them to frame the issue in terms of how epistemic “access” to the world is mediated. A third strategy for dissolving the realism question challenges its underlying commitment to the independence of meaning and truth, a strategy pursued in different ways by Donald Davidson, Robert Brandom, John McDowell, John Haugeland, and myself. The fourth and most encompassing strategy shows that realists and antirealists are thereby committed to an objectionably antinaturalist conception of scientific understanding, in conflict with what the sciences themselves have to say about our own conceptual capacities.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.031 |
| Scholarly communication | 0.010 | 0.018 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".