Bibliographic record
Abstract
Si Jean-Guy Goulet semble avoir, en partie, centré sa réflexion anthropologique autour des façons de s’entendre et vivre ensemble, c’est dans la perspective de ses travaux que l’auteure propose de réunir les questions de l’intersubjectivité, du terrain et de la rencontre avec les animaux au sein de son article. Elle raconte comment une approche intersubjective à Old Crow lui a permis d’aller à la rencontre, non seulement des humains vuntut gwich’in, mais surtout de leurs chiens qui peuplent presque tout autant le village. À partir de données ethnographiques, le plus souvent corporellement et sensiblement recueillies à travers l’empirisme de la rencontre, elle décrit comment ces chiens ont su s’inviter sur la scène ethnographique, non pas en tant que simples figurants, mais bien en tant qu’acteurs. Après avoir posé les enjeux méthodologiques et théoriques d’une telle ethnographie, elle explique comment des concepts initialement érigés par et pour des humains pour appréhender et comprendre l’altérité, trouvent également sens et pertinence dans des mondes animaux.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.036 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.005 |
| 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".