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Backshoring, Local Sweatshop Regimes and CSR in India

2014· article· en· W2160329418 on OpenAlexfundno aff
Alessandra Mezzadri

Bibliographic record

VenueCompetition & Change · 2014
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsnot available
FundersEconomic and Social Research CouncilUniversity of British ColumbiaMinistry of Textiles, Government of IndiaBritish Academy
KeywordsSweatshopCorporate social responsibilityCommodity chainCommodityProduction (economics)BusinessProduct (mathematics)EconomicsMarket economyEconomic systemPolitical scienceMicroeconomicsLaw

Abstract

fetched live from OpenAlex

Deploying an approach to chain analysis concerned with regional differentiation and backshoring, this article investigates the regional complexities of the garment commodity chain in India and its multiple local sweatshop regimes to illustrate the limitations of corporate social responsibility (CSR) norms. First, the article shows that India's distinctively regional organization of production and product specialization, arising from different local historical legacies of production, reproduces labour outcomes that prevent the effectiveness of CSR. Second, it shows that the backshoring practices used by a powerful group of Pan-Indian buyer-exporters, who increasingly behave like global buyers, further reproduce the logic of the local sweatshop, hence reinforcing the limitations of corporate approaches to labour standards.

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)0.001
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.016
Threshold uncertainty score0.033

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0020.013
Scholarly communication0.0050.002
Open science0.0000.004
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.030
GPT teacher head0.256
Teacher spread0.226 · 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

Citations42
Published2014
Admission routes1
Has abstractyes

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