Fishful Thinking: Rhetoric, Reality, and the Sea Before Us
Bibliographic record
Abstract
Fisheries science and management have been shrouded in controversy and rhetoric for over 125 yrs.Human reliance on fish through history (and even prehistory) has impacted the sea and its resources.Global impacts are manifest today in threatened food security and vulnerable marine ecosystems.Growing consumer demand and subsidized industrial fisheries exacerbate ecosystem degradation, climate change, global inequities, and local poverty.Ten commonly advocated fisheries management solutions, if implemented alone, cannot remedy a history of intense fishing and serial stock depletions.Fisheries policy strategies evaluated along five performance modalities (ecological, economic, social, ethical, and institutional) suggest that composite management strategies, such as ecosystem-based management and historically based restoration, can do better.A scientifically motivated solution to the fisheries problem can be found in the restorable elements of past ecosystems, if some of our present ideology, practices, and tastes can be relinquished for this historical imperative.Food and social security can be enhanced using a composite strategy that targets traditional food sources and implements customary management practices.Without binding laws, however, instituting such an ethically motivated goal for fisheries policy can easily be compromised by global market pressures.In a restored and productive ecosystem, fishing is clearly the privilege of a few.The realities of imminent global food insecurity, however, may dictate a strategy to deliberately fish down the food web, if the basic human right to food is to be preserved for all.
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.
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 | 0.013 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.018 | 0.079 |
| Scholarly communication | 0.015 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.007 | 0.012 |
| Insufficient payload (model declined to judge) | 0.005 | 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".