Contested Norms in Inter-National Encounters: The ‘Turbot War’ as a Prelude to Fairer Fisheries Governance
Bibliographic record
Abstract
This article is about contested norms in inter-national encounters in global fisheries governance. It illustrates how norms work by reconstructing the trajectory of the 1995 ‘Turbot War’ as a series of inter-national encounters among diverse sets of Canadian and European stakeholders. By unpacking the contestations and identifying the norms at stake, it is suggested that what began as action at cross-purposes (i.e. each party referring to a different fundamental norm), ultimately holds the potential for fairer fisheries governance. This finding is shown by linking source and settlement of the dispute and identifying the shared concern for the balance between the right to fish and the responsibility for sustainable fisheries. The article develops a framework to elaborate on procedural details including especially the right for stakeholder access to regular contestation. It is organised in four sections: section 1 summarises the argument, section 2 presents the framework of critical norms research, section 3 reconstructs contestations of fisheries norms over the duration of the dispute, and section 4 elaborates on the dispute as a prelude to fairer fisheries governance. The latter is based on a novel conceptual focus on stakeholder access to contestation at the meso-layer of fisheries governance where organising principles are negotiated close to policy and political processes, respectively. The conclusion suggests for future research to pay more attention to the link between the ‘is’ and the ‘ought’ of norms in critical norms research in International Relations theories (IR).
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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 | 0.025 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.107 |
| Scholarly communication | 0.014 | 0.015 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.006 | 0.009 |
| 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".