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Record W4401577282 · doi:10.1111/glob.12501

Brokering Political Corporate Social Responsibility: Production Network Intervention Programmes in Post‐Reform Myanmar's Garment Industry

2024· article· en· W4401577282 on OpenAlex

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

VenueGlobal Networks · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicGlobal trade, sustainability, and social impact
Canadian institutionsCarleton University
FundersKongelige Danske Videnskabernes Selskab
KeywordsPoliticsProduction (economics)Intervention (counseling)Corporate social responsibilityBusinessKnowledge productionEconomic growthPolitical sciencePublic relationsEconomicsLawPsychology

Abstract

fetched live from OpenAlex

ABSTRACT Political corporate social responsibility (CSR) research focuses on how companies leverage their CSR efforts to improve public goods provision in countries where public governance is lacking. Previous studies, due to their limited analytical scope, have not thoroughly examined the dynamic nature of these governance gaps. Another missing puzzle is how certain actors in these countries, through their regular operations, independently facilitate political CSR opportunities—such as production network intervention (PNI) programmes. Positioning PNI programmes as brokers of political CSR, we investigate four such programmes in the Myanmar's garment industry during the early years of the country's reform. We conduct neo‐Gramscian analysis to examine how these programmes attempted to establish cultural and ideological leadership over CSR discourse and practice in the industry. We analyse the manufacturers’ responses to this contestation, which evolved with the introduction of a minimum wage, and discuss the theoretical and practical implications of this study.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.449
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0010.001
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 head0.024
GPT teacher head0.286
Teacher spread0.262 · 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