A Survey-Based Qualitative Analysis of the Institutional Structures and Policy Measures in the Shea Sector of Ghana
Bibliographic record
Abstract
Shea is an important tree crop for women in the three impoverished northern regions of Ghana and is considered to be a major source of poverty alleviation in these regions. The crop is picked in the wild as nuts mainly by women who sell the nuts to processors. These nuts are processed into butter and soap for local use and/or for exports. Institutional structures, from a variety sources including the cultural environment, community support systems and the State regulatory and support mechanisms, shape the opportunities, constraints and obstacles facing women pickers and local processors who rely on shea as an important source of income and economic empowerment. Both State and non-State institutions in Ghana have designed various policy interventions and programmes for the shea sector with the objective of reducing market failures of the sector and to improve incomes of shea-producing households. Based on a relatively large survey of 405 shea-producing households in selected districts of the Northern Region of Ghana, this paper discusses the institutional structures and policy measures in the shea sector in Ghana. From the perspective of the survey respondents, there is not enough coordination of programmes and policies among the various institutions in the shea sector. Respondents feel that the shea sector is largely unregulated; various actors take actions mainly for their own benefits and not necessarily for the benefit of the whole sector. Organised groups of shea-producing households are more likely to improve their chances of being impacted by programmes and policies of State and non-State institutions than unorganized individual shea-producing households.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".