Multinational Enterprises, Employee Safety and the Socially Responsible Supply Chain: The Case of Bangladesh and the Apparel Industry
Bibliographic record
Abstract
Abstract This article address the issue of employee safety and the social responsibility of multinational apparel retailers who contract with Bangladesh manufacturers in their global supply chain. Both the Alliance for Bangladesh Worker Safety and the Accord on Fire and Building Safety in Bangladesh have been identified as the two primary facilitators for global apparel industry efforts to actively address this serious human rights issue; thus, they have the potential to help drive the success of the industry's corporate citizenship efforts to successfully manage the issue of fire and building safety in Bangladesh. The article further explores these relationships within the context of the “global corporate citizenship” concept, and develops a rationale for the limits of a socially responsible supply chain. In the context of global corporate citizenship, the article describes the existing state of these two industry organizations remediation efforts to ensure a stable supply chain in Bangladesh, and offers an analysis of existing industry nonmarket strategy approaches to improving contractor's factory fire and building safety environments for their employees. Lastly, a comprehensive set of nonmarket strategies for multinational apparel retailers is recommended when addressing their global corporate citizenship commitments to a safe working environment for Bangladesh garment manufacturing employees.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".