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Record W2886510544 · doi:10.1061/9780784481288.060

A Comparison of Safety Climate and Safety Performance between Ontario’s Residential and Heavy Civil Construction Sectors

2018· article· en· W2886510544 on OpenAlex
Yuting Chen, Brenda McCabe, Douglas Hyatt

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

VenueConstruction Research Congress 2018 · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsHudbay Minerals (Canada)University of Toronto
Fundersnot available
KeywordsOccupational safety and healthWork (physics)Fire safetyEnvironmental healthBusinessTransport engineeringEngineeringMedicineCivil engineering

Abstract

fetched live from OpenAlex

Between 2013 and 2016 in the Province of Ontario, Canada, 739 surveys were collected from 70 residential construction sites, and 342 surveys were collected from 34 heavy civil construction sites. Safety climate and safety performance of these two sectors were compared. Overall, residential respondents reported slightly more safety incidents than heavy civil sites but they had very similar safety climate scores. For both sector respondents, “cut/puncture,” “strains/sprains.” “headache/dizziness,” and “persistent fatigue” are the most frequently experienced physical injuries; “slip/trip/fall on same level,” “exposure to chemicals,” and “overexertion while handling/lifting/carrying” are the most frequently experienced unsafe events. The role of management and supervisor safety commitment in improving safety and the impact of work pressure on physical safety outcomes and job stress are highlighted.

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)0.004
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0040.005
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.130
GPT teacher head0.490
Teacher spread0.360 · 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