MétaCan
Menu
Back to cohort
Record W2906598090 · doi:10.1016/j.jsr.2018.12.008

An illusion of objectivity in workplace investigation: The cause analysis chart and consistency, accuracy, and bias in judgments

2018· article· en· W2906598090 on OpenAlexafffund
Carla L. MacLean, J. Don Read

Bibliographic record

VenueJournal of Safety Research · 2018
Typearticle
Languageen
FieldHealth Professions
TopicOccupational Health and Safety Research
Canadian institutionsSimon Fraser UniversityKwantlen Polytechnic University
FundersSocial Sciences and Humanities Research Council of CanadaWorkSafe Victoria
KeywordsObjectivity (philosophy)IllusionPoison controlConsistency (knowledge bases)PsychologyChartHuman factors and ergonomicsInjury preventionSuicide preventionOccupational safety and healthSocial psychologyForensic engineeringApplied psychologyEngineeringStatisticsComputer scienceCognitive psychologyMedical emergencyMedicineMathematicsArtificial intelligenceEpistemology

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.103
metaresearch head score (Gemma)0.395
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.103
Threshold uncertainty score0.543

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.1030.395
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0100.004
Science and technology studies0.0030.022
Scholarly communication0.0080.015
Open science0.0020.006
Research integrity0.0030.006
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 head0.297
GPT teacher head0.547
Teacher spread0.249 · 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

Citations15
Published2018
Admission routes2
Has abstractno

Explore more

Same venueJournal of Safety ResearchSame topicOccupational Health and Safety ResearchFrench-language works237,207