Debating nature : science and decision-making in pacific salmon hatcheries
Bibliographic record
Abstract
The presence of hatcheries in the management of Pacific salmon in North America, in the fact they constitute a literal interface between humans and nature, is unique in the species that humans actively manage for food or other benefit. This, in tandem with the scientific criticism they face through salmon management literature, situate hatcheries as an opportune subject for the study of decision-making about science in natural resource management or conservation settings. This thesis explores the broader question of how decisions are made regarding new science in Pacific Salmon hatcheries through the study of two separate contexts at two different scales. The first research chapter addresses the question: how have hatcheries and the scientific research that is focused on them been discussed in the public sphere? This is achieved through a newspaper content analysis with a focus on the examination of framing mechanisms in the words of the journalists and those they feature. This chapter reveals that hatcheries are portrayed and debated based on the benefits they provide, but also on the scientific and economic concerns that people have about them. These risks and benefits were directly pitted against each other in a media debate regarding a question involving interpretation of the United States Endangered Species Act, which involved brought much of the scientific research about hatcheries into the public sphere. The second research chapter focuses in at a much finer scale, on the hatchery management system on Canada's west coast. In this chapter, perspectives on selective breeding as a tool in broodstock management are explored through interviews with individuals working for hatcheries and the Department of Fisheries and Oceans - Canada's salmon management authority. This chapter reveals that, though individuals hold various views about the merit and acceptability of selective breeding, there is a unanimous desire within the sample of hatchery management staff to pursue decisions that advance the naturalness of hatchery fish and overall salmon populations. Together, these inquiries contribute to a fuller understanding of the social dynamics involved in decision-making about science in the management of Pacific salmon hatcheries.
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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 | 0.019 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.036 |
| Scholarly communication | 0.012 | 0.010 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.006 |
| 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".