Bibliographic record
Abstract
Forests are the locus of conflict in Canada, where the rights and interests of Indigenous peoples can collide with those of the province (Crown) and the forest industry. Efforts to achieve “forest justice” — or sufficient forest rights to meet the self-governance goals of Indigenous peoples, and in ways that ensure collective resilience — have typically been driven by the principle of reconciliation. In this paper, these “forest justice” efforts were examined through a tripartite justice theory, involving recognition, representation, and redistribution pathways. This paper documents that recognition involves Crown efforts to recognize Indigenous peoples’ forest rights, including ownership, and access and use rights; representation includes improved participation in forest governance; and redistribution covers the allocation of timber harvesting rights and forest lands to Indigenous peoples. This study documents that the bulk of “forest justice” activity is focused on the redistribution of timber harvesting licences, the volume of which has doubled over the last two decades. These three paths to justice must all be pursued together to deliver “forest justice”, and to move towards “reconciliation in the woods”. Recommendations are offered to support forest justice in practice.
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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 | 0.010 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".