Valeurs autochtones et modèles forestiers
Bibliographic record
Abstract
Au Québec, la foresterie s’effectue majoritairement en territoires traditionnels autochtones. Il est de plus en plus reconnu qu’une gestion durable des forêts doit tenir compte des valeurs autochtones. Par contre, respecter ces valeurs dans les processus de gestion est difficile à mettre en pratique. Les auteurs de cet article s’attaquent à ce problème en expliquant comment six modèles de gestion reflètent les valeurs des Innus d’Essipit associées au territoire. En utilisant des groupes de discussion, quatre valeurs principales sont identifiées, soit : le maintien de l’identité innue, le développement de la communauté, le respect du Nitassinan et la gouvernance innue. En examinant six différents modèles forestiers, on peut voir qu’aucun ne répond à l’ensemble des valeurs identifiées. Cela suggère qu’une « foresterie innue » devra s’inspirer d’une diversité de modèles en les adaptant et en les intégrant, afin de développer une foresterie sur mesure qui soit culturellement appropriée.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.013 | 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".