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Record W2992763742 · doi:10.1177/0019793919894240

Global Purchasing as Labor Regulation: The Missing Middle

2019· article· en· W2992763742 on OpenAlex
Matthew Amengual, Greg Distelhorst, Danny Tobin

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 · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsMicrodata (statistics)PurchasingBusinessSupply chainReputationIncentiveFlexibility (engineering)AuditOrder (exchange)Labour economicsIndustrial organizationEconomicsMarketingMicroeconomicsCensusAccountingFinance

Abstract

fetched live from OpenAlex

Do purchasing practices support or undermine the regulation of labor standards in global supply chains? This study offers the first analysis of the full range of supply chain regulatory efforts, integrating records of factory labor audits with purchase order microdata. Studying an apparel and equipment retailer with a strong reputation for addressing labor conditions in its suppliers, the authors show that the retailer persuaded factories to improve and terminated factories with poor labor compliance. However, the authors also find that purchase orders did not increase when labor standards improved. If anything, factories whose standards worsened tended to see their orders increase. Contrary to the conventional wisdom, this “missing middle” in incentives for compliance appears unrelated to any cost advantage of noncompliant factories. Instead, lack of flexibility in supplier relationships created obstacles to reallocating orders in response to compliance findings.

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 score0.893
Threshold uncertainty score0.952

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.051
GPT teacher head0.288
Teacher spread0.237 · 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