Global shark fins in local contexts: multi-scalar dynamics between Hong Kong markets and Mid-Atlantic fisheries
Bibliographic record
Abstract
We analyze multi-scalar social, economic, and policy dynamics of shark fin production and consumption through Hong Kong, the world's leading shark fin entrepôt, and U.S. Mid-Atlantic artisanal fisheries in New Jersey (NJ), a U.S. state that enacted a shark-fin retail ban in 2021. Trade statistics point to a rise in shark fin circulation to Hong Kong in recent years supplied through global pathways of production. Global discourses of overconsumption and shark finning in Asia have shaped U.S. state environmental policies banning shark fin retail. However, interviews with shark fin retailers and consumers in Hong Kong point not to undifferentiated Asian consumption, but instead indicate gendered, classed, and intergenerational dynamics that undergird consumption and bear on production elsewhere. New Jersey fisheries, once an exporter to Hong Kong, enacted a state-wide shark fin retail ban in response to global defaunation and anxieties related to Asian fishing and consumption practices. Interviews and focus group discussions illustrate how the ban has resulted in a practice artisanal fishers call
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".