Reconciling social justice and ecosystem-based management in the wake of a successful predator reintroduction
Bibliographic record
Abstract
The reintroduction of a previously extirpated predator can engender conflict when the reintroduced species depletes customary fisheries to which indigenous communities have constitutionally protected rights. In the case of sea otter (Enhydra lutris) recovery on the west coast of North America, not only is Canada’s Species at Risk Act in conflict with Indigenous rights, but it also illuminates gaps in the principles of ecosystem-based management (EBM), such as equity and social justice. Broadly, we ask in this paper how EBM might be advanced if Indigenous communities were viewed as components of ecosystems having rights to a sustainable future equal to other components. Specifically, we explore evidence of sea otter management among precontact Northwest Coast societies and a contemporary co-managed system proposed by the Nuu-chah-nulth First Nations that would combine research with refinement of traditional hunting practices. We show that barriers persist through lack of knowledge of past controlled hunts, ignorance of recent experiences of successful community-based clam management, distrust of Indigenous capacity to self-manage or co-manage a hunt, and divergent values among actors.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".