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Record W2289060045 · doi:10.1080/10301763.2016.1144386

Dead quiet in the hinterlands: the construction of workplace injuries in western Canadian newspapers, 2009–2014

2016· article· en· W2289060045 on OpenAlexaffabout
Bob Barnetson, Jason Foster

Bibliographic record

VenueLabour & Industry a journal of the social and economic relations of work · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicCrime, Deviance, and Social Control
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNewspaperOccupational safety and healthInjury preventionRepresentation (politics)Work (physics)Suicide preventionPoison controlPublic relationsCriminologyPolitical scienceMedicineAdvertisingSociologyBusinessEngineeringEnvironmental healthLaw

Abstract

fetched live from OpenAlex

Media reports profoundly misrepresent the nature of workplace injuries and fatalities in Canada. This study uses a new dataset comprising 409 urban and rural newspaper reports in western Canada to confirm the over-representation of fatalities, injuries to men, acute physical injuries, and injuries in blue-collar occupations found in earlier exploratory work. This misleading social construction of injuries may skew public policy and management decision-making about injury prevention. The study also confirms the existence of three key media frames: injuries are “under investigation,” “human tragedies,” and “before the court.” Together, these frames cast workplace injuries as isolated events that happen to “others” for which no one is responsible (except maybe the worker), thereby suggesting that the public need not be concerned about workplace safety. Contrary to expectations, no significant differences were found between the reporting of urban and rural newspapers.

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 score0.231
Threshold uncertainty score0.964

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 studies0.0000.001
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 head0.014
GPT teacher head0.268
Teacher spread0.254 · 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

Citations1
Published2016
Admission routes2
Has abstractyes

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