What does “First Nation deep roots in the forests” mean? Identification of principles and objectives for promoting forest-based development
Bibliographic record
Abstract
We often hear about the resistance of First Nation (FN) communities to the industrial model of forestry, but we hear less about what they wish to achieve. Translating FN perspectives into concepts that are understood by the mainstream society can help inform current and future forest policies. Such translation can support initiatives that seek ways to increase FN participation in the forest sector. This paper documents one process of translation. It identifies the principles and objectives for forest-based development of the Essipit Innu First Nation in Quebec, Canada, reflective of the deep roots that anchor the Essipit to their territory. Based on participatory research carried out between January and July 2013, we identify 34 objectives folded into three core FN principles: Nutshimiu–Aitun (identity–territoriality), Mishkutunam (sharing–exchange), and Pakassitishun (responsibility–autonomy). Our analysis shows that the economic aims of the dominant forestry model are too narrow for FN communities. This paper contributes to expanding FN engagement in forestry through management and economic approaches that are better adapted to their culture and values.
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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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.024 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".