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Record W1977989573 · doi:10.5751/es-05113-170418

Of Fish and Fishermen: Shifting Societal Baselines to Reduce Environmental Harm in Fisheries

2012· article· en· W1977989573 on OpenAlexaffvenue
Mimi E. Lam

Bibliographic record

VenueEcology and Society · 2012
Typearticle
Languageen
FieldEnvironmental Science
TopicWildlife Conservation and Criminology Analyses
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsHarmFisheryFish <Actinopterygii>BusinessFisheries managementNatural resource economicsEnvironmental resource managementEnvironmental scienceFishingEconomicsPolitical scienceBiology

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.021
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.031
GPT teacher head0.257
Teacher spread0.226 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations24
Published2012
Admission routes2
Has abstractyes

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