The North American Helpline initiative in Bangladesh for garment workers
Bibliographic record
Abstract
INTRODUCTION: After a series of garment factory disasters that had taken place in Bangladesh, the Alliance for Bangladesh Worker Safety (Alliance) was formed by 29 large North American retail companies to improve worker safety in Bangladesh- the second largest ready-made garments producing country in the world. AIMS: This report focuses on Alliance's Worker Empowerment initiative-Worker Helpline and examines the types, contents and volume of calls received by it. METHODS: All published reports of Alliance that are available online were retrieved. Data from each quarter (Q) in 2017, 2018, and 2019 were extracted in terms of (1) Total calls (2) Substantive calls, and (3) Safety calls (Urgent and Non-urgent). RESULTS: By 2019, Q3 Helpline covered 1.5 million workers in 1091 factories. In Q1 2017, there was 1 call made per 73 workers and 20 calls made per a factory whereas in Q3 2019 there was 1 call per 171 workers and 8 calls coming from a factory. In terms of safety calls, there was 0.59 calls/factory in Q1 2017 but went down to 0.17 calls/factory in Q3 2019. Helpline in 2019 Q3 received 1283 substantive calls; of that 189 were safety calls which included 18 urgent and 171 non-urgent calls. In Q1 of 2017, 32% factories did not make any calls and in Q3 2019, 62% of factories did not make any calls at all. CONCLUSIONS: The worker empowerment initiative- Helpline-in Bangladesh initiated by the North American companies remained underutilized.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.029 | 0.005 |
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".