Building Back Sustainably: COVID-19 Impact and Adaptation in Newfoundland and Labrador Fisheries
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it