Asymmetry in knowledge, attitudes, and practices of shark conservation advocates: sustainable shark fisheries vs. bans on fisheries and trade
Bibliographic record
Abstract
Abstract Current shark conservation and management conflicts represent an underrecognized expression of long-standing debates over whether the goal of modern conservation should be sustainable exploitation of natural resources or maximum possible preservation of wilderness and wildlife. In the developing world, exploitation of fisheries resources can be essential to food security and poverty alleviation, and management is typically focused on sustainably maximizing economic benefits. This approach aligns with traditional fisheries management and the perspectives of most surveyed scientific researchers who study sharks. However, in Europe and North America, sharks are increasingly venerated as wildlife to be preserved irrespective of conservation status, resulting in growing pressure to prohibit exploitation of sharks and trade in shark products. To understand the causes and significance of this divergence in goals, we surveyed 155 shark conservation focused environmental advocates from 78 environmental non-profits, and asked three key questions: (1) where do advocates get scientific information? (2) Does all policy-relevant scientific information reach advocates? and (3) Do advocates work towards the same policy goals identified by scientific researchers? Findings suggest many environmental advocates are aware of key scientific results and use science-based arguments in their advocacy, but a small but vocal subset of advocates report that they never read the scientific literature or speak to scientists. Engagement with science appears to be the key predictor of whether advocates support sustainable management of shark fisheries or bans on shark fishing and trade in shark products. Conservation is a normative discipline, and this analysis more clearly articulates two distinct perspectives in shark conservation. Most advocates support the same evidence-based policies as academic and government scientists, while a smaller percentage are driven more by moral and ethical beliefs, and may not find scientific research relevant or persuasive. A values-based perspective is also a valid approach to conservation, but claiming that it is a science-based approach while misrepresenting the science is problematic. This suggests possible alternative avenues for engaging diverse stakeholders in productive discussions about shark conservation.
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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 | 0.020 | 0.052 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".