Transforming nature's subsidy: Global markets, Burkinabè women and African shea butter
Bibliographic record
Abstract
In this dissertation I examine the sustainability of shea butter projects in the centre-west region of Burkina Faso, West Africa. Shea butter derives from the Vitellaria paradoxa tree, which grows in the semi-arid African savanna. Women living in this region have long collected and processed shea nuts into butter for household consumption and sale. Advocates for gender equality and sustainable development are thus pursuing the growing global demand for shea butter in the cosmetics industry to enhance the incomes of these impoverished female producers. Via ‘shea butter projects', international donors and non-governmental organisations are facilitating the integration of shea butter producers into international markets, including those based on Fair Trade. As I argue in this dissertation, an understanding of the sustainability prospects of these aid interventions is essential to develop projects and policies that maximize the benefits of Fair Trade for producer communities. Conceptually, the framework guiding my study draws upon scholarship on feminist political ecology, traditional ecological knowledge, commodity chains, and sustainable livelihoods. The methodology adopted involves observation, semi-structured and informal interviews, participatory wealth rankings, and shea tree mapping and measurements in 62 fields under five different types of land uses. Fieldwork was conducted among Gurunsi, Moose, and FulBe women and men in the rural village of Prata, the peri-urban village of Lan, and the town of Léo, which are located in the centre-west province of Sissili. My study addresses three specific objectives. The first is to investigate the nature of shea tree management and conservation in rural areas of the centre-west region of Burkina Faso, and the extent to which these are gendered. Findings related to this objective show that Gurunsi and Moose agriculturalists hold detailed knowledge of shea agroforestry that is not visibly differentiated according to gende
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".