MétaCan
Menu
Back to cohort
Record W4297384848 · doi:10.3233/wor-211126

Overview of the “Gender & Work” track at the IEA 2021 congress

2022· article· en· W4297384848 on OpenAlexafffund
Marie Laberge, Andréane Beaupré, Karen Messing, Jessica Riel, Sandrine Caroly

Bibliographic record

VenueWork · 2022
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversité de MontréalUniversité du Québec à MontréalCentre Hospitalier Universitaire Sainte-Justine
FundersSocial Sciences and Humanities Research Council of CanadaCanadian Institutes of Health Research
KeywordsHuman factors and ergonomicsContext (archaeology)Scope (computer science)Work (physics)PsychologyPopulationMedical educationPsychological interventionEngineeringPublic relationsEngineering ethicsApplied psychologyPolitical scienceMedicinePoison controlEnvironmental healthComputer scienceGeographyMechanical engineering

Abstract

fetched live from OpenAlex

BACKGROUND: The International Ergonomics Association (IEA) is an international federation of associations created in 1959, whose mission is to extend the scope of ergonomics research and intervention to all spheres of society in order to improve human well-being. OBJECTIVE: This article presents an overview of the main research papers that were presented at the 21st Triennial IEA 2021 Conference. METHOD: A total of 23 talks, from nine countries, were presented over four sessions. These papers were summarized based on reading the abstracts and taking notes at the time of the oral presentation. RESULTS: The themes of these sessions were: 1) Knowledge Transfer, Gender and Ergonomics 2) Approaching Ergonomic Interventions with a Sex/Gender Lens: Designing Training for Ergonomists 3) Ergonomic Studies of Atypical Work and Vulnerable Population Through a Sex/Gender Lens: Toward Better Understanding of Context and Risks, for Better Prevention and 4) Gender and Occupational Risks (Part 1: Exposure and Risk Perception; Part 2: Strategies to Manage Risk). CONCLUSION: Ergonomists are beginning to understand that they have the qualifications and legitimacy to play a role in reducing workplace health inequities and helping to make workplaces inclusive and rich of all the workers' diversity. The four sessions of the Gender and Ergonomics TC have moved ergonomics practice a step closer to that goal.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.322
Threshold uncertainty score0.996

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)0.0050.000

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 head0.045
GPT teacher head0.233
Teacher spread0.187 · 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.

Study designNot applicable
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
Published2022
Admission routes2
Has abstractyes

Explore more

Same venueWorkSame topicErgonomics and Human FactorsFrench-language works237,207