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Record W4220948403 · doi:10.1061/9780784483985.070

Scale Equivalence in Canada and the United States for Interpersonal Conflicts at Work and Individual Resilience in the Construction Sector

2022· article· en· W4220948403 on OpenAlexaffabout
Yuting Chen, Brenda McCabe, Jun Wang, Douglas Hyatt

Bibliographic record

VenueConstruction Research Congress 2022 · 2022
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsHudbay Minerals (Canada)University of Toronto
Fundersnot available
KeywordsScale (ratio)Interpersonal communicationEquivalence (formal languages)Resilience (materials science)Work (physics)Psychological resilienceSocial psychologyPsychologyGeographyEngineeringMathematicsDiscrete mathematicsCartography

Abstract

fetched live from OpenAlex

Interpersonal conflicts at work (ICW) and individual resilience (IR) that describes a person’s positive psychological capacity for performance improvement have the potential to affect construction safety performance. However, few research has been conducted to investigate these two factors on construction sites, e.g., how often ICW occurs on construction sites and whether the occurrence frequency is distributed similarly across countries. It is also necessary to examine whether workers from different countries interpret ICW and IR conceptually similar, which is a precondition for any comparison. By surveying 420 US construction workers and 837 Canadian construction workers, this study conducted measurement equivalence tests and compared the frequency of ICW on the surveyed construction sites. The results show that US and Canadian construction workers interpreted ICW and IR conceptually similar, although with different demographic background. In addition, US respondents reported fewer conflicts with their supervisors, which may be related to the slightly higher age, lower participation in safety committees, and fewer supervisors on site.

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)0.008
metaresearch head score (Gemma)0.025
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.199

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.025
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0040.006
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.433
Teacher spread0.318 · 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 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

Citations0
Published2022
Admission routes2
Has abstractyes

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