Bibliographic record
Abstract
This article examines the Wolastoqiyik Wahsipekuk's green sea urchin fishery to explore the long-term implications of diversification strategies in response to ecological and economic precarities in the Canadian fishing industry. Framing diversification as a creative practice developed by commercial fishermen to navigate these vulnerabilities, it highlights how institutional frameworks shape and constrain such efforts. Drawing on ethnographic fieldwork conducted in Eastern Quebec during the summer of 2021, the article focuses on the specific regulatory context in which this initiative unfolds. Unlike some other First Nations in Canada, the Wolastoqiyik fishery remains closely tied to the models and oversight of Canada's Department of Fisheries and Oceans (DFO). An ethnographic analysis of the fishery's sociomaterial entanglements reveals both the promise and the limitations of diversification. Grounded in political ecology, the article argues that while expanding into emerging species may offer short-term relief, it cannot constitute a viable long-term response to the structural dimensions of the current ecological crisis. This calls for more transformative approaches to fisheries governance—approaches that challenge inherited management systems and engage with an era increasingly defined by socio-ecological unpredictability.
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 | 0.001 |
| Science and technology studies | 0.011 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".