Bibliographic record
Abstract
Our special guest today is Stevan Harnad, a prominent figure in the Open Access movement. Author of the famous 'Subversive Proposal', founder of 'Psycoloquy' and the Journal Behavioral and Brain Sciences, creator and administrator of AmSciForum, one of the main coordinators of CogPrints initiative – the list could be stretched far beyond that – he doesn't really need introduction for anyone not wholly a stranger to the story of the Open Access movement. A cognitive scientist specialising in categorization, communication andconsciousness, Harnad is Professor of cognitive sciences at the Université du Québec à Montréal and University of Southampton, external member of the Hungarian Academy of Sciences and doctor honoris causa, University of Liège. But even his polemics with John Searle about the Chinese Room didn't become as famous and influential as his Open Access advocacy.
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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.020 | 0.011 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.009 | 0.023 |
| Insufficient payload (model declined to judge) | 0.023 | 0.006 |
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".