How “wild” are hatchery salmon? Conservation policy and the contested framing of nature in Canada and the United States
Bibliographic record
Abstract
The idea of the Anthropocene presents a paradox for conservation: to restore and protect wild species and ecosystems, greater human intervention is required through efforts such as artificial propagation. This paradox is evident in efforts to conserve Pacific salmon. Salmon hatcheries produce millions of salmon to augment wild populations and sustain fishing industries, but emerging knowledge about salmon genomics has called into question the “wildness” of hatchery salmon. This article examines how the scientific uncertainties regarding wild species are contested by a range of stakeholders and how particular frames become concretized in policy frameworks. Despite the significance of laws and policies to the governance of such hybrid species, they have received limited attention. Drawing on archival documents, legislation, policies, government reports, and media sources, we conduct a cross-national comparative analysis of how wildness is framed in policy debates in Canada and the United States. We find that hatchery-born salmon occupy a position at the threshold of scientific and cultural definitions of wildness and this ambiguity facilitates political contests among groups with divergent interests in conservation and views of the human–nature relationship. As a result, hatchery salmon have been regulated differently across time and geographic and jurisdictional space. These findings contribute to an expanding literature on conservation in the Anthropocene and managing wild species.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.020 | 0.019 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".