Viewpoint: Back to the future for fisheries, where will we choose to go?
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
Abstract We present a view on global marine fisheries that emphasizes mitigating the conflict between sustainability and the scale of industrial exploitation driven by the demand of continuous economic growth. We then summarize the current state of global fisheries. Finally, we advocate strongly for scaling back industrial fisheries, most of which are non-sustainable. This can be achieved through eliminating the harmful, capacity-enhancing subsidies that prop up industrial fisheries to continue operating despite declining fish stocks. Instead, we propose to support well-managed, locally owned and operated small-scale fisheries, which generally contribute more to local employment and food security. We stress that contrary to deep-seated opinion in the fishing industry and among politicians, reducing overfishing by eliminating overcapacity in fishing fleets will actually lead to greater, not reduced catches. This would address part of the increased global seafood demand over the coming decades, which is driven by population and wealth growth. This seems counterintuitive, but is supported by fisheries science, data and experiences. Thankfully, we are beginning to see that some of these changes are being pursued by a growing number of countries and international institutions.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.019 | 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 it