Perspectives of Cree land users on the performance of fish habitat compensation projects in Eeyou Istchee
Bibliographic record
Abstract
Eeyou Istchee is the ancestral home of the Cree Nation in Northern Quebec, and is changing rapidly due to extensive industrial development, including forestry, hydropower, and mining. Efforts are being made to offset the resulting damages to freshwater ecosystems via fish habitat compensation projects, after efforts are first made to avoid and minimize harm, with the goal of No Net Loss of fish habitat productivity. Currently, the success and relevance of Canadian fish habitat compensation projects is primarily assessed through scientific knowledge, while their success and relevance in Eeyou Istchee according to Cree Indigenous knowledge is currently unknown. We used Q-methodology to assess the perspectives of Cree land users regarding fish habitat compensation in Eeyou Istchee, to evaluate the need, success, and relevance of these projects, and provide recommendations for future fish habitat compensation projects within the region that consider the perspectives of Cree land users. Our findings express three main recommendations: (1) prioritization of local knowledge and governance, (2) effective remediation of water contamination, and (3) integration of the tallyman system with fish habitat compensation. Addressing these recommendations raised by Cree land users is likely to increase the success and relevance of fish habitat compensation projects in Eeyou Istchee.
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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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".