Bibliographic record
Abstract
Patrick Rysiew* Department of Philosophy, The University of Victoria, P.O. Box 3045, Victoria BC V8W 3P4 Canada (Received 8 October 2013; final version received 25 October 2013) By Reid’s own account, ‘That the natural faculties, by which we distinguish truth from error, are not fallacious’ (FP#7), has a special place among the First Principles of Contingent Truths. Some have found that claim puzzling, but it is not. Contrary to what’s usually assumed, certain FPs preceding FP#7 do not already assert the better part of what FP#7 explicitly states. FP#7 is needed because there is nothing epistemological in the FPs that precede it; and its special place among the FPs is a straightforward consequence of its being both perfectly general and distinctively epistemological. Keywords: first principles; reliability; Thomas Reid; Philip De Bary; Keith Lehrer 1. Introduction Central to Reid’s philosophy is common sense and its defense; central to the latter are the First Principles he articulates. But Reid’s presentation gives rise to various problems of interpretation – for instance, whether first principles are general or particular, whether they are principles of truth or of evidence, in what sense they can really be said to be ‘self-evident’, in what sense they are things ‘we all believe’, and so on.1 Another such concern, and the one to be addressed here, is just how to understand one of the First Principles of Contingent Truths (hereinafter, ‘First Principles’) and its relation to the rest. This is First Principle #7, which speaks to the ‘non-fallaciousness’ of ‘the natural faculties, by which we distinguish truth from error’.
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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 | 0.013 | 0.038 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.019 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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".