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<scp>T</scp>he last generation? Perspectives of inshore fish harvesters from Change Islands, Newfoundland

2013· article· en· W1778463555 on OpenAlexaffvenueabout
Derek Smith, Kelly Vodden, Maureen Woodrow, Ahmed Khan, Bojan Fürst

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicCoastal and Marine Management
Canadian institutionsUniversity of OttawaMemorial University of NewfoundlandCarleton University
FundersWorld Bank Group
KeywordsFishingRationalization (economics)FisheryBusinessFish <Actinopterygii>Fisheries managementCommercial fishingCompetition (biology)Natural resource economicsGeographyEnvironmental resource managementEnvironmental planningEconomicsEcology

Abstract

fetched live from OpenAlex

Abstract Our investigation examines the perspectives of fish harvesters on key challenges facing the inshore fishery in Newfoundland and Labrador. The findings, based primarily on in‐depth interviews with harvesters in the town of Change Islands, show that fishers are deeply concerned about ineffective regulations, low prices for their catch, and rationalization policies. They explain how existing regulations restrict traditional cooperative fishing practices and impose rules that are not suited for local environmental conditions. Low prices for fish landed, they argue, are caused in part by lack of competition among buyers and a bonus system that favours larger enterprises. These conditions, combined with policies aimed at reducing the fishing fleet and barriers to youth involvement, threaten the long‐term survival of coastal communities. Overall, current policies keep inshore harvesters on the sidelines of an increasingly industrialized fishery. Local fish harvesters have valuable local, place‐based knowledge that can be used to develop more effective fishery management policies and marketing strategies, and in this article we share their recommendations on how to build more sustainable fisheries. However, traditional fishing communities—along with the potential social, cultural, economic, and environmental benefits of smaller‐scale, community‐based fishing—need to become more visible for these changes to occur.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.170
Threshold uncertainty score0.343

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0110.003
Scholarly communication0.0040.002
Open science0.0010.002
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.010
GPT teacher head0.175
Teacher spread0.165 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations13
Published2013
Admission routes3
Has abstractyes

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