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Record W91081352 · doi:10.4000/pistes.2193

“We work by the second !” Piecework remuneration and occupational health and safety from an ethnicity- and gender-sensitive perspective

2008· article· en· W91081352 on OpenAlexfundvenueaboutno aff
Stéphanie Premji, Katherine Lippel, Karen Messing

Bibliographic record

VenuePerspectives interdisciplinaires sur le travail et la santé · 2008
Typearticle
Languageen
FieldHealth Professions
TopicWorkplace Health and Well-being
Canadian institutionsnot available
FundersUniversity of Ottawa
KeywordsRemunerationWorkforceOccupational safety and healthFactory (object-oriented programming)CommissionWork (physics)Perspective (graphical)Production (economics)Compensation (psychology)BusinessPublic relationsPsychologyPolitical scienceEconomicsEngineeringSocial psychologyLawFinanceComputer science

Abstract

fetched live from OpenAlex

Few qualitative studies have described the mechanisms by which piecework influences health. We present the results of 25 interviews conducted between 2004 and 2006 in a large garment factory in Montreal. We describe the workforce, made up in large part of women and immigrants, the requirements and constraints of production, workers’ strategies favouring production and those favouring health, and the management and impact of the health problems experienced by workers. In addition, we compare the experience of piecework to its representation by various stakeholders (employers, workers, decision-makers, doctors) as reported in 62 decisions regarding compensation claims for work-related health problems rendered by the Commission des lésions professionnelles (C.L.P.) between 2000 and 2007, decisions pertaining to the garment industry and mentioning piecework. We examine the causes and discuss the implications of our results.

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)0.004
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.104
Threshold uncertainty score0.206

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0090.016
Scholarly communication0.0030.002
Open science0.0010.003
Research integrity0.0010.001
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 head0.033
GPT teacher head0.395
Teacher spread0.361 · 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 designQualitative
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

Citations9
Published2008
Admission routes3
Has abstractyes

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