Building Economically, Socially and Ecologically Resilient \nFisheries and Coastal Communities: A Policy Paper
Bibliographic record
Abstract
Building resilient fisheries and coastal communities for Newfoundland and Labrador’s future is one of the most important opportunities and challenges of our time. In many of our coastal communities, fisheries continue to be the major source of employment and wealth generation, a crucial part of rural economies, of our identity and our cultural heritage. The owner-operator small and medium-scale enterprises and fish plants generate most of this wealth and employment. In recent decades our fisheries and coastal communities have weathered some severe storms, including the 1990s collapse of our groundfish stocks. Their capacity to respond to such challenges without fundamental cultural, social and ecological change is evidence of their resilience, which is now in serious jeopardy. It is threatened by unfounded claims that our fisheries are broken and the best way to fix them is by turning fisheries quotas and licenses into commodities that can be bought and then sold to the highest bidder. It is also suggested that we get rid of policies that limit vertical integration, although such policies have kept access to many (not all) of our fish resources widely dispersed around our coasts. As a result they have both enhanced the employment and wealth they produce for the province, and anchored much of that wealth in the households of people who work in the industry and in the communities where they live. This is not the time to jettison them.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".