Diversité ethnolinguistique, inclusion et croissance : éléments de réflexion
Bibliographic record
Abstract
Le traitement des langues nationales se concrétise souvent par des exigences constitutionnelles. Il est plausible que dans certains cas que l'on dépense beaucoup par locuteur avec peu de résultats. Nonobstant ceci il est pertinent de maintenir des engagements fondateurs d'un pays La reconnaissance de droits linguistiques peut être vu comme une forme de decentralization[...].Nous concluons donc qu'une politique publique favorisant la diversité linguistique chez les individus par des investissements de création et de maintien du capital humain linguistique est une politique favorisant la croissance économique. Une politique d'uniformisation linguistique ou de dépenses publiques importantes pour des très petits groupes linguistiques ne contribuent pas à la croissance économique.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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 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".