Contesting corporate responsibility in the Bangladesh garment industry: The local factory owner perspective
Bibliographic record
Abstract
In the developing economy of Bangladesh, local factory owners in the garment industry have felt great pressure to improve factory safety, but the costs for those improvements are not shared by the global apparel firms that wield immense influence over them. Consequently, we examine whether multi-stakeholder initiatives (MSIs), as vehicles of corporate social responsibility (CSR), offer platforms for democratic oversight or merely serve as new arenas to exercise corporate power. Given their role in connecting global and local contexts and their history of safety incidents, local factory owners possess a unique perspective on the impact and contested nature of CSR in global supply chains. This article presents a qualitative study of MSIs in the Bangladesh garment industry, particularly after the Rana Plaza collapse. Through interviews with local factory owners and executive managers, we explore the reasons behind their opposition to CSR as exercised by global apparel firms, and the contestation of those practices by their local business association. Our findings lead us to conclude that garment industry MSIs are unlikely to be effective without labor procurement practices that harmonize global and local interests to mitigate the competitive pressures on local factory owners.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".