Managing the linkage between export development and poverty reduction
Bibliographic record
Abstract
Purpose In the world's quest to eradicate poverty, the means to get there are not fully understood, nor are they universally agreed upon. However, most would accept that the link between trade and development in general and exports and poverty reduction in particular needs to be strengthened and effects better understood. The purpose of this paper is to suggest that a management framework exists by which the linkage between exports and poverty reduction can be better understood and as a consequence strengthened. Design/methodology/approach Drawing on the International Trade Centre's Priority Setting Framework to Export Development, a hypothetical strategy has been prepared for the Rwandan coffee sector, which reinforces the export development and poverty reduction linkage. Findings Many strategies stop short at providing detailed action steps that result in the project's objectives being effectively implemented and its impact being measured. Practical implications The framework can be used to guide national strategy‐makers, trade support organizations, sector associations, NGOs and the donor community in formulating, and more importantly, implementing poverty reduction initiatives in the context of export development. Originality/value The paper draws upon a methodology applied in trade related technical assistance and attempts to demonstrate this framework, which primarily addresses competitiveness issues can be rigorously applied to the design and implementation of an export‐led poverty reduction strategy.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".