Bibliographic record
Abstract
Cet article se veut une réflexion sur l’apport des langues minoritaires et minorisées à la diversité culturelle mondiale, aux connaissances portées par celles-ci et à la façon dont elles contribuent à mieux comprendre les capacités de l’esprit humain. Il y est question de l’épineuse notion de relativité linguistique et du jeu entre l’influence de la langue sur la pensée et le reflet des catégories conceptuelles des groupes humains dans la langue qu’ils parlent. Les patrons de lexicalisation, les mots et les grammaires culturellement spécifiques cités dans ce texte contribuent à illustrer la diversité linguistique à laquelle nous accédons par le biais des langues. La sauvegarde de cette diversité est évidemment implicite dans mes propos, mais j’insiste fortement sur l’importance du rôle des usagères et usagers des langues dans ce processus.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 teacher head, 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".