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The Changing Political Economy of Occupational Health and Safety in Fisheries: Lessons from Eastern Canada and South Africa

2012· article· en· W1540428044 on OpenAlexaffabout
Dana Howse, Mohamed F. Jeebhay, Barbara Neis

Bibliographic record

VenueJournal of Agrarian Change · 2012
Typearticle
Languageen
FieldHealth Professions
TopicEmployment and Welfare Studies
Canadian institutionsWorkplace Health, Safety and Compensation CommissionMemorial University of NewfoundlandUniversity of Toronto
Fundersnot available
KeywordsLivelihoodOverfishingOccupational safety and healthPoliticsGlobalizationFisheryEconomic growthBusinessPolitical scienceAgricultureEconomyGeographyEconomicsFishing

Abstract

fetched live from OpenAlex

Growing literatures on the political economy of occupational health and safety and on the political economy of contemporary fisheries need to be better integrated to help us see how neoliberal globalization and overfishing are interacting with gender, race and class relations in regional fish harvesting and processing workplaces to affect occupational health risks and the consequences. This contribution uses findings from occupational allergy and asthma (OAA) research among seafood processing workers in Eastern Canada and the west coast of South Africa to enhance our understanding of the political economy of occupational health and safety concerns in contemporary fisheries. Expanding features of global fisheries, mediated by regional histories, are changing the vulnerabilities of processing workers to OAA by influencing production volumes and products; local, regional and international divisions of labour; employment precariousness and access to prevention, health and compensation services. Affected workers' common perception that they must choose between their livelihoods and their health is to some degree a reality in these fisheries.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.199
Threshold uncertainty score0.959

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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)0.0000.000

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 head0.162
GPT teacher head0.377
Teacher spread0.214 · 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.

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

Citations17
Published2012
Admission routes2
Has abstractyes

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