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Record W3020484078 · doi:10.1177/0019793920911905

Voice in Supply Chains: Does the Better Work Program Lead to Improvements in Labor Standards Compliance?

2020· article· en· W3020484078 on OpenAlex
Kelly Pike

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.

Bibliographic record

VenueIndustrial and Labor Relations Review · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsYork University
Fundersnot available
KeywordsCompliance (psychology)Work (physics)Factory (object-oriented programming)Empirical researchBusinessPublic relationsLabour economicsPsychologyPolitical scienceEconomicsSocial psychologyComputer scienceEngineering

Abstract

fetched live from OpenAlex

Using a six-year study of Better Work Lesotho (BWL), this article examines whether the ILO’s Better Work initiative leads to improvements in labor standards compliance. Data include 55 focus group discussions conducted with 426 workers during four waves of data collection between 2011 and 2017. In-depth qualitative research with workers before, during, and after BWL reveals the root causes underlying noncompliance. Findings indicate that improvements across a number of compliance areas are enabled by collective worker voice mechanisms established by BWL at the factory level. Workers also highlight additional positive impacts of these improvements beyond the workplace. The author concludes that worker voice is essential to long-term sustainable improvements in labor standards compliance. This study makes an empirical and a methodological contribution by demonstrating the importance of worker voice in both the implementation of Better Work and its evaluation and impact.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.822
Threshold uncertainty score0.468

Codex and Gemma teacher scores by category

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

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 head0.061
GPT teacher head0.321
Teacher spread0.261 · 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