Bibliographic record
Abstract
Sociobiologie et anthropologie : les obstacles actuels à l'intégration Pour repenser les rapports de la sociobiologie à l'anthropologie, il faut revoir l'anthropologie elle-même. Soi-disant étude de la variabilité sociale et culturelle, elle a échoué dans cette tâche, d'une part à cause de sa définition de la culture et, d'autre part, à cause de ses présupposés holistes. En appréciant ce que veut vraiment dire étudier la variabilité, c'est-à-dire comparer rigoureusement, on débouche sur une anthropologie de la variabilité qui s'inspire des visées classiques. Affranchie de tout présupposé holiste et guérie de ses malaises conceptuels, cette anthropologie c néo-classique ", pour ainsi dire, nous permet de reconsidérer ses rapports avec la sociobiologie sur la base d'une analogie avec les rapports historiques entre biologie et chimie, autour de ce personnage central qu'est le grand physiologiste Claude Bernard.
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 | 0.034 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.008 | 0.059 |
| Scholarly communication | 0.027 | 0.030 |
| Open science | 0.003 | 0.019 |
| Research integrity | 0.011 | 0.018 |
| 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".