Integrating Poverty, Gender and Environmental Concerns into Value Chain Analysis: A Conceptual Framework and Lessons for Action Research
Bibliographic record
Abstract
Many contemporary development solutions and policy prescriptions place emphasis on the potential for closer integration of poor people or areas with global markets. But the prospects for the reduct-ion of chronic poverty depend in great measure on the nature of the broader economic processes that, according to how they are configured, can either exacerbate or alleviate poverty. These pro-spects also depend on the forms of local economic growth that impact on the lives of the poor. Since the mid 1990s, a literature has emerged on value chains that has helped increase our understanding of how firms and farms in developing countries are integrated in global markets. Studies using the global value chain approach examine different types of value chain governance and the opportunities they provide for technological or functional upgrading of traders and producers in developing countries. But few value chain studies have succeeded in explicitly documenting the impact of value chain activities on poverty, gender and the environment. In this light, the paper develops a conceptual framework that can help overcome the shortcomings highlighted so far in 'stand-alone' value chain, livelihoods and environmental analyses by integrating the 'vertical' and 'horizontal' aspects of value chains that affect poverty and sustainability. This framework is used to draw lessons for external interventions in value chains targeted at small pro-ducers and other weak actors in developing countries, particularly the kinds of interventions known as 'action research' which puts emphasis on strategic and political approaches to achieving sustained improvements for disadvantaged groups. A companion paper to the present one develops a strategic framework and practical methods to guide action research in value chains (Riisgaard et al., 2008). The entire methodology will be tested during 2008-09 by seven action research projects targeted at poor rural producers in Africa and Asia. All projects form part of the Rural Poverty and Environment programme of the International Development Research Centre and are carried out as part of the RPE research theme 'integrate poverty and environmental concerns into value chain analysis' under the guidance of the Overseas Development Institute, London.
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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 | 0.031 | 0.012 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.010 | 0.094 |
| Scholarly communication | 0.025 | 0.035 |
| Open science | 0.007 | 0.011 |
| Research integrity | 0.011 | 0.009 |
| Insufficient payload (model declined to judge) | 0.005 | 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".