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Framing and blaming: construction of workplace injuries by legislators in Alberta, Canada

2013· article· en· W1986259684 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueInternational Journal of Occupational and Environmental Health · 2013
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsAthabasca University
Fundersnot available
KeywordsNarrativeFraming (construction)LegitimacyLegislationIgnorancePublic relationsLegislatureCredibilityFatalismOccupational safety and healthPolitical scienceGovernment (linguistics)PoliticsPoison controlMedicineEnvironmental healthLawEngineering

Abstract

fetched live from OpenAlex

BACKGROUND: Legislators in the Canadian province of Alberta have successfully resisted pressure to increase state injury-prevention efforts. OBJECTIVES: This study seeks to identify the narratives used by legislators to manage political pressure for increased injury-prevention efforts. METHODS: Narrative analysis of legislative transcripts from 2000 to 2012. RESULTS: Three narratives are identified in the data: (1) injuries are caused by ignorance and inattention, (2) workplaces are safe and getting safer, and (3) risk is inevitable and mitigation is (too) expensive. Each narrative has 2-4 subcomponents. CONCLUSIONS: The consistency of the messages delivered by legislators over time suggests an intentional effort to frame workplace injury in ways that manage political pressure for greater state efforts to prevent workplace injuries while maintaining the government's legitimacy. The narratives used by legislators draw on widely held beliefs about workplace injuries, including the careless worker myth and the notion that safety pays.

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.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.215
Threshold uncertainty score0.944

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)0.0000.000

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 head0.017
GPT teacher head0.360
Teacher spread0.343 · 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