Alternative Regimes of Transnational Environmental Certification: Governance, Marketization, and Place in Alaska's Salmon Fisheries
Bibliographic record
Abstract
Transnational certification and ecolabeling programs have become an important new site of environmental governance, as well as an emerging arena for action and conflict in international trade. This paper explores the implementation of Marine Stewardship Council (MSC) certification in salmon fisheries in the US state of Alaska in the early 2000s, the growing opposition within the industry to MSC certification through periods of reassessment, and the emergence of an alternative Alaska certification initiative in 2011. It suggests that these shifts were rooted in struggles over understudied third-party certification and labeling processes that we conceptualize as marketized governance. The paper further shows how certification and labeling can obscure and expose competing industry interests and power relations, provoke struggles over fisheries' social representation, and open up novel avenues for cooperation and change, indicating ambiguity in the social and cultural implications of neoliberal modes of governance. Finally, the paper suggests that the ascendancy of the MSC has sparked the emergence of new political and economic geographies of certification and ecolabeling in Alaska and other jurisdictions where place-specific and other initiatives vie for governance and market legitimacy.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".