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Record W4248337018 · doi:10.3402/polar.v28i1.6098

From good to eat to good to watch: whale watching, adaptation and change in Icelandic fishing communities

2009· article· en· W4248337018 on OpenAlexfundno aff
Níels Einarsson

Bibliographic record

VenuePolar Research · 2009
Typearticle
Languageen
FieldHealth Professions
TopicIndigenous Studies and Ecology
Canadian institutionsnot available
FundersEuropean Science FoundationArctic Institute of North AmericaNational Science Foundation
KeywordsWhalingFishingFisheryMarine conservationGeographyCommercial fishingWhaleArcticTourismFishing industryEcologyBiology

Abstract

fetched live from OpenAlex

Arctic and North Atlantic fishing communities may seem unlikely candidates for a viable whale-watching industry, because of the prevalent traditional consumptive attitudes towards marine mammals and their uses. The topic of this paper is the introduction of an internationally growing industry of whale watching in a fishing village in north-east Iceland, and how local inhabitants reconcile opposing views on whales, whaling and the new cetacean tourism. The paper also discusses the conflict between fishermen and marine mammals, and how it is managed in an area where fishing is still a mainstay of the economy, and where marine mammals are seen by many as competitors for scarce resources, and even as pests. This anthropological case study is used to address wider issues of adaptation, community viability and resilience in small resource-dependent coastal settlements, coping with rapid social and ecological change.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.096
Threshold uncertainty score0.190

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 studies0.0040.003
Scholarly communication0.0020.001
Open science0.0000.002
Research integrity0.0010.001
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 head0.311
GPT teacher head0.493
Teacher spread0.183 · 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

Citations6
Published2009
Admission routes1
Has abstractyes

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