Of Fish and Fishermen: Shifting Societal Baselines to Reduce Environmental Harm in Fisheries
Bibliographic record
Abstract
If reasonable fishery harvests and environmental harms are specified in new regulations, policies, and laws governing the exploitation of fish for food and livelihoods, then societal baselines can shift to achieve sustainable fisheries and marine conservation. Fisheries regulations can limit the environmental and social costs or harms caused by fishing by requiring the fishing industry to pay for the privilege to fish, via access fees for the opportunity to catch fish and extraction fees for fish caught; both fees can be combined with a progressive environmental tax to discourage overcapitalization and overfishing. Fisheries policies can be sustainable if predicated on an instrumental and ethical harm principle to reduce fishing harm. To protect the public trust in fisheries, environmental laws can identify the unsustainable depletion of fishery resources as ecological damage and a public nuisance to bind private fishing enterprises to a harm principle. Collaborative governance can foster sustainable fisheries if decision-making rights and responsibilities of marine stewardship are shared among government, the fishing industry, and civil society. As global food security and human welfare are threatened by accelerating human population growth and environmental impacts, decisions of how to use and protect the environment will involve collective choices in which all citizens have a stake -and a right.
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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.016 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".