Bibliographic record
Abstract
Must a neurophysiological account of human behaviour be a mechanist one? This is the question I would like to address myself to in this paper. The common sense of our age, informed to some degree by the scientific tradition, seems to fall naturally into a Kantian antinomy on this question (as Marjorie Grene has pointed out), that is, both the thesis and the antithesis seem to be grounded in solid argument. On one hand, it appears natural to proceed on the assumption that there is no upper limit to our ability to account for the functioning of ourselves and other animate organisms in terms of body chemistry and neurophysiology, and it is equally natural to assume that such accounts will be mechanistic – particularly in view of the sterility of rival approaches, such as vitalism. It even seems plausible to argue that ‘mechanistic explanation’ is a pleonasm, for any other kind of account appears rather to sidestep the problems of explanation; it does not, in other words, increase our ability to predict and control the phenomena as explanations should. On the other hand, common sense is alarmed by the prospect of a complete mechanistic account of behaviour; not just alarmed practically, because of the unscrupulous use to which this knowledge might be put, but alarmed metaphysically, if I may use this term, because of what such an explanation would show about us.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.016 |
| Scholarly communication | 0.006 | 0.014 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.009 | 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".