Ten years of Inuit co-management: advancing research, resilience, and capacity in Nunatsiavut through fishery governance
Bibliographic record
Abstract
Community-based approaches have risen to prominence in fisheries governance as decision makers have recognized the importance of local perspectives, and Indigenous Peoples have pursued their right to self-determination. In Canada, some Indigenous Peoples have pursued a formalized approach to co-management through land claim agreements. The Torngat Joint Fisheries Board (TJFB) is one such co-management arrangement that focuses on fisheries management in Nunatsiavut, a land claim area in northern Labrador, Canada. This research examines how the TJFB’s work contributes to fisheries governance in the region, and subsequently, how co-management is placed in terms of supporting greater self-determination for Indigenous peoples in resource governance. To understand the TJFB’s role, this research examined 12 years of recorded meeting minutes from 2010 to 2021, highlighting the activities in which the TJFB engages, and how those activities have changed over time. Inductive content analysis was used to understand the activities undertaken by the TJFB, highlighting their actions as well as the strengths and weaknesses of the co-management board in practice. The analysis found that the TJFB plays important roles in research, drafting recommendations, and public education, and that their activities support greater participation from fisheries stakeholders. Land claim–based co-management has a significant impact on how Indigenous sovereignty operates and how it will evolve into the future. The TJFB’s efforts to increase research capacity in the region, push focus towards the socio-cultural dimensions of fisheries management, and strengthen the political voice of the region represent an important move toward self-determination in Nunatsiavut’s commercial fisheries.
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 | 0.006 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.017 | 0.010 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.009 |
| 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".