Bibliographic record
Abstract
The current conception of sustainable fisheries focuses on single "stocks" targeted by industrial fisheries to supply growing global markets, including those for fishmeal.Sustainable fisheries should be reimagined to minimize exploitation and prioritize artisanal and subsistence fishing that feeds people.Industrial fisheries, which currently account for approximately 75% of global catch [1], began in the 1890s, when the UK deployed the first steel-hulled steam trawlers in its coastal waters.These capital-and energy-intensive behemoths, within 2 decades, decimated costal fish populations around the British Isles and so moved their operations further and further offshore.This was the start of the globalization of industrial fishing, driven by a recurring pattern of fisheries collapses, and compensatory geographic expansion.One century later, researchers demonstrated that industrial fisheries had a devastating impact on fish populations globally [2][3][4].Governments, civil society organizations, university researchers, international bodies, and the private sector responded to rampant overfishing by promoting "sustainable fisheries," i.e., fisheries operating such that their catch could be maintained indefinitely.However, despite discussions about ecosystem-based management, each of these groups defined (and, to some extent, implemented) the sustainability of fisheries primarily as a management goal to enable a maximal exploitation of single "stocks" of wild aquatic animals, rather than the maintenance of the ecosystems in which these "stocks," or rather populations, are embedded.The emphasis on the management of single stocks has meant that the concept of sustainable fisheries has been too narrow to achieve commonsense notions of sustainability, given the well-documented propensity of industrial gears, such as trawling, the industrial gear par excellence, to strongly degrade marine ecosystems.The focus on quota setting has come at the cost of broader considerations about delegitimizing destructive fishing practices, restoring ecosystems, addressing overcapacity, eliminating fisheries subsidies, reducing impacts on climate change, and understanding the lives of the animals we exploit and our relationship to them.Many fisheries labeled as "sustainable" will not be sustained due, e.g., to the modifications they inflict on the ecosystems.Consider the Marine Stewardship Council (MSC), the leading and most visible global fisheries certification scheme, as an example.The MSC has certified the Gulf of Maine lobster fishery, which has recently achieved consistent catches.But the fishery exists in its present state because previous fisheries have collapsed the Gulf of Maine
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 | 0.011 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.017 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.008 | 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".