Forêt identitaire, forêt partagée : Trajectoire d’une recherche participative chez les Anicinapek de Kitcisakik (Québec, Canada)
Bibliographic record
Abstract
L’élaboration de stratégies d’aménagement forestier adaptées aux cultures autochtones figure parmi les défis de la foresterie moderne au Canada. Les Anicinapek de Kitcisakik (Québec, Canada) ont choisi de s’engager dans un processus de recherche, en collaboration avec une équipe universitaire interdisciplinaire et un regroupement de partenaires industriels. Cette initiative a permis d’entreprendre un dialogue interculturel dans le but de mieux définir les fondements d’une foresterie dite « autochtone » qui s’appuie sur une démarche d’élaboration de critères et d’indicateurs locaux pour l’évaluation et l’amélioration de scénarios d’aménagement forestier. Cet article présente les grandes étapes de cette recherche participative ainsi que les principaux défis éducatifs, méthodologiques et stratégiques qui ont émergé en cours de route.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.006 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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 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".