Bibliographic record
Abstract
Cet article fait le point sur la dimension totémique des symboles utilisés comme signatures par les Amérindiens du Nord-Est lors des traités avec les Européens. La théorie du totémisme est alors abordée, en particulier les analyses de Lévi-Strauss et de Testart. Si celles-ci semblent antagoniques, une conciliation est cependant proposée selon l’objectif poursuivi : un regard ethnographique favorisera l’analyse classificatoire du totémisme (Lévi-Strauss) tandis qu’une étude comparative des différentes pratiques des sociétés claniques vis-à-vis de la nature intégrera les formes sociales et apparemment individuelles de rapport à la nature (Testart). Cependant, ni l’une ni l’autre de ces approches ne conçoivent le symbole comme un signifiant qui, en garantissant la parole du signataire grâce au lien établi dans l’imaginaire collectif entre le symbole et la Loi qui donne son identité sociale au groupe, permet au discours d’alliance d’opérer.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.014 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 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".