Building Back Sustainably: COVID-19 Impact and Adaptation in Newfoundland and Labrador Fisheries
Bibliographic record
Abstract
The coronavirus pandemic, which started in late 2019, is one of the devastating crises that has affected human lives and the economies of many countries across the globe. Though economies have been affected, some sectors (such as food and fisheries sectors) are more vulnerable and prone to the deleterious impacts of the COVID-19 pandemic. This paper highlights the various disruptions (safety at workplace, loss of harvest and processing activity, loss of export opportunities and income) faced by the Newfoundland and Labrador fisheries due to several restrictive measures (especially on mobility, social distancing, quarantine, and, in extreme cases, lockdown) to curtail the spread of the virus. Additionally, this paper makes a case that Newfoundland and Labrador fisheries can be managed sustainably during and after the pandemic by suggesting practical recommendations borrowed from two sustainability frameworks (Canadian Fisheries Research Network and the EU Setting the Right Safety Net framework) for managing fisheries in Canada and the European Union.
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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 | 0.001 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".