Safety Risks, Institutional Responses, and Narratives in Fishery Management: A Case Study of the 2023 Commercial Glass Eel Season in Nova Scotia, Canada
Bibliographic record
Abstract
This article investigates the criminogenic features and safety challenges in Canada’s commercial glass eel fishery focusing on the 2023 season in Nova Scotia. The fishery, characterized by high value density and nocturnal harvesting, has become increasingly vulnerable to illegal fishing. The research analyzes over 1,400 pages of documents obtained through Access to Information and Privacy requests, including interdepartmental communications within Fisheries and Oceans Canada, public infraction reports, and fishery officer patrol data. Using social network analysis, the study maps communication flows among key groups mainly institutional across three operational phases. It reveals a disconnect between institutional narratives and public concerns. The study identifies two major findings. First, the crisis seemed foreseeable due to the fishery’s biological and organizational traits – narrow harvest windows, accessible gear, and high market volatility – yet institutional responses were delayed and reactive. Second, institutional emphasis on media relations and symbolic enforcement actions, such as listing seizures and patrols without contextual detail, suggests a prioritization of image management over substantive risk mitigation. Ultimately, the research highlights the dual role of the state as both regulator and crisis responder, and the importance of aligning institutional actions with public safety concerns. It calls for more proactive, transparent, and community-informed governance strategies in managing high-risk, high-value fisheries like the Canadian glass eel fishery.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.022 | 0.009 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".