Bibliographic record
Abstract
Cet article présente des arguments en vue d’une résolution des problèmes liés à la question de l’intentionnalité naturelle dans une perspective biosémiotique. En confrontant les théories de l’information au modèle évolutionniste dominant en biologie, l’auteur montre les insuffisances du réductionnisme néodarwinien dans les cas d’adaptation sans évolution au sens strict de la sélection naturelle. Ainsi est-ce l’agentivité qui se retrouve au coeur de l’interrogation : comment a-t-elle pu émerger au sein de la nature ? Est-elle suffisante pour définir le vivant ? Le modèle biosémiotique permet d’envisager un continuum évolutif au sein du vivant, dont le facteur de croissance, et l’effet, serait l’accroissement de la liberté sémiotique, c’est-à-dire l’amélioration, pour les organismes vivants, de leurs compétences interprétatives, liberté dont l’effet bénéfique sur la valeur sélective s’observe à travers une sophistication des modes de communication inhérents à leur organisation.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".