Pathways to implement the \nSmall-Scale Fisheries Guidelines in \nNewfoundland and Labrador, Canada
Bibliographic record
Abstract
This thesis examines pathways found in barriers and opportunities to implement the Voluntary Guidelines for Securing Sustainable Small-Scale Fisheries in Newfoundland and Labrador, Canada. The 34th Committee on Fisheries in 2020 declared Canada as the fifth country to become Friends of the Small-Scale Fisheries Guidelines. At about the same time, the Canadian government began to develop the Blue Economy strategy, wrapping up its consultation phase in 2022. Yet, it is unclear how the Small-Scale Fisheries Guidelines can be implemented in the Canadian context, or how they will factor into plans for a Blue Economy strategy. Through use of interactive governance theory and written from the perspective of an active commercial fishing person, this research explores challenges and opportunities to implement the Small-Scale Fisheries Guidelines into Canadian fisheries governance, using Newfoundland and Labrador as a case study. Implementation of the Small-Scale Fisheries Guidelines is vital for advancement towards Blue Justice in Canada. Blue Justice, coined by Moenieba Isaacs at the 3rd World Small-Scale Fisheries Congress in 2018 held in Thailand is a concept centering on social justice to carve out just and secure coastal spaces for small-scale fisheries people (Jentoft et al. 2022). Blue Justice means securing access to resources and protections for small-scale fisheries people and being inclusive of small-scale fisheries people in decision-making surrounding development of the world’s oceans (Jentoft et al. 2022). This thesis addresses the governance problem of implementation of the Small-Scale Fisheries Guidelines from both the legal and fisher perspectives, with an investigation of laws and policies, as well as operations and practices. Together, these perspectives show the potential for the Small-Scale Fisheries Guidelines to guide and shape future policy, planning and decision-making in Canadian small-scale fisheries, including in a forthcoming Blue Economy strategy. This research finds that there are key \nopportunities and pathways for advancing alignment, including strengthening legal recognition and representation of small-scale fisheries people, securing access and tenure rights for small-scale fisheries in the ocean space, and integrating social and community principles in decision-making for small-scale fisheries.
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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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.022 | 0.008 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| 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".