Bibliographic record
Abstract
This chapter evaluates the progress and pitfalls of several distinctive types of corporate social responsibility (CSR) vis-à-vis the cotton-poverty relationship, and also discusses country-level factors that can impede the uptake of CSR or its efficacy. It hones in on a global norm-building effort known as the Better Cotton Initiative (BCI) and on the work that has been done to establish a Cotton Made in Africa (CMIA) product label. A case study of Tanzania’s organic cotton movement is presented, and an account of the ways that the conventional cotton-buying scene in Tanzania is consequential for attempts to introduce CSR and make it viable is elaborated. The chapter contends that ‘hardcore’ approaches to responsibility involving third-party certification have a greater poverty-reducing potential than lighter-touch alternatives. However, the more stringent CSR variants are by no means a cure all. They could yet be squeezed out as competition to establish poverty-reducing best practices intensifies, or face considerable growth constraints if the evident disincentives to heightened levels of responsibility in Africa remain unchecked. A case is also made below that developments in the cotton issue area underscore the need for a more nuanced categorization of CSR types. Moving forward, analysts of these new phenomena should consider paying particular attention to the implications of private regulatory competition and look more closely at the ways that traditional philanthropic giving is supportive of the new responsibility or detracts from particular variants of it. These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.005 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.016 | 0.001 |
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".