Bibliographic record
Abstract
Je propose une solution à l’énigme de Kripke en distinguant tout d’abord les usages matériel et intentionnel du verbe « croire ». L’usage intentionnel est ensuite analysé à partir de l’usage matériel. Une croyance intentionnelle n’est rien d’autre qu’une croyance matérielle réitérée, c’est-à-dire, une croyance matérielle que l’agent entretient au sujet de ses propres croyances. L’énigme de Kripke peut alors être résolue pourvu que l’on accepte aussi l’inscriptionnalisme. On est ainsi en mesure de reconnaître que Pierre a bel et bien des « croyances » contradictoires, mais seulement au sens matériel de l’expression. Il n’a cependant pas de croyances intentionnelles contradictoires puisque les contenus de croyance sont des inscriptions et qu’il se croit lui- même en rapport avec ces inscriptions.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.032 |
| Scholarly communication | 0.006 | 0.016 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.006 | 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".