Bibliographic record
Abstract
This paper aims to challenge the idea claimed by Putnam in his Dewey Lectures that internal realism presupposed sense data theory so that it would have been unable to account for the fundamental intuition of common sense realism that perception gives us cognitive access to reality. Rather, I argue that Putnam’s writings from the period of internal realism indicate that it (internal realism) already presupposed a form of direct realism of the kind he puts forth in the Dewey lectures. I support my thesis with a demonstration of the implication of direct realism in the refutation of the brain-in-a-vat hypothesis that occurs in the first chapter of Reason, Truth and History, as well as with various passages from the philosopher’s writings of the time when he defended internal realism. I also argue, contrary to what the philosopher seems to assert in his Dewey Lectures, that his model-theoretic argument against metaphysical realism does not involve sense data theory. After noting the very strong resemblance between the theses of his common sense realism of the 1990s and those of his internal realism of the 1980s, I hypothesize that Putnam pretended to renounce internal realism in order to allow himself to rephrase his original position so as to avoid being misinterpreted as a form of idealism or fact constructivism, as was the case with his initial statement.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.004 | 0.010 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".