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Record W4409352625 · doi:10.1002/csr.3204

Corporate Social Responsibility and Wages in the Global Apparel Supply Chain

2025· article· en· W4409352625 on OpenAlexaff
Jinsun Bae, Sarosh Kuruvilla

Bibliographic record

VenueCorporate Social Responsibility and Environmental Management · 2025
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsCarleton University
Fundersnot available
KeywordsCorporate social responsibilityBusinessClothingSupply chainSocial responsibilityFast fashionIndustrial organizationCommerceMarketingPublic relations

Abstract

fetched live from OpenAlex

ABSTRACT Through corporate codes of conduct, apparel companies seek to ensure that basic labor rights and standards are upheld in their global supply chains. Wages, a key subject in corporate codes, have received less attention in part due to the difficulty of accessing firm‐level wage data from suppliers. In this paper, we analyze longitudinal wage data from a global auditing company and cross‐sectional wage data from a global retailer's supply chain to evaluate whether wages paid to supply chain workers in six major apparel production countries have been sufficient. We find that workers in these countries received wages higher than the legal minimum but substantially below living wage estimates. We also test a long‐held assumption that wages are likely to increase in the presence of stronger exercise of workers' associational rights. We find that suppliers are likely to pay higher wages relative to the legal minimum in countries with greater institutional support for freedom of association and collective bargaining.

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)0.001
metaresearch head score (Gemma)0.007
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 score0.010
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0040.000

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 head0.027
GPT teacher head0.254
Teacher spread0.227 · 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

Citations2
Published2025
Admission routes1
Has abstractyes

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