Bibliographic record
Abstract
Abstract Purpose – In Bangladesh, 30 percent of the population lives beyond the "last mile" of traditional distribution networks and serving this rural low‐income population with socially useful goods is a huge challenge. The purpose of this paper is to present one of the most innovative and successful cases of its kind in the world, a social enterprise rural distribution model originally developed by CARE Bangladesh and the Bata Shoe Company, to illustrate the possibility of combining market‐based solutions to poverty with socially responsible business growth. Design/methodology/approach – This in‐depth case study was developed over the course of three field visits to Bangladesh between November 2009 and September 2010 based on 25 face‐to‐face interviews with rural sales women, Bata employees and CARE staff as well as participant observation and review of project documents and media reports. Findings – The case provides insights into the origins, lessons learned and key success factors of viable rural sales agent distribution networks serving the poor. A key tension to be managed is keeping the costs of the network down while ensuring that every member is adequately incentivized. Social implications – The 3,000 women sales agents in rural Bangladesh engaged with the Rural Sales Program have benefited from earning viable incomes in contexts where opportunities for employment and empowerment of women are limited. Rural populations have gained affordable access to socially beneficial goods such as fortified foods, seeds, daily necessities and shoes. Companies have benefited from learning how to adapt their product offerings to meet the needs of low‐income customers. Originality/value – Where rural sales initiatives elsewhere have faced challenges, this case is the first published account of the origins of how CARE, Bata, and other companies established a viable and scalable rural sales agent distribution network for the commercial benefit of companies and the economic and social benefit of poor women and their customers.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.086 | 0.011 |
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".