The socio-economic contribution of non-timber forest products to rural livelihoods in Sub-Saharan Africa: knowledge gaps and new directions
Bibliographic record
Abstract
SUMMARY The majority of Sub-Saharan Africa's population relies on forest products for subsistence uses, cash income, or both. In the case of non-timber forest products (NTFPs), it is imperative to 1) clearly understand the socio-economic contributions that they make to rural livelihoods in order to 2) design policies, interventions, and business ventures that serve to safeguard forest assets for the poor in a targeted manner. Based on existing literature, this article highlights the quantitative contributions that NTFPs have made to rural household incomes in several forested, Sub-Saharan African countries. Reasons for a paucity of data on this front are discussed. The article then identifies five broad socioeconomic factors (location, wealth status, gender, education, and seasonality) affecting levels of dependency on NTFPs by rural households, and calls for a better understanding of the linkages between these five factors in order for targeted policies on poverty alleviation in forest-dependent communities to be developed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".