Bois de Villages (Niger): Report of an Investigation Concerning Socio-Cultural and Political-Economic Aspects of the Forest Phase of the Project and Design Recommendations for a Possible Second Phase
Bibliographic record
Abstract
"Two weeks' field investigation of IDRC village woodlots in the '3M' Arrondissements of Zinder Department supports throe conclusions. First, villagers express substantial and sharpening interest in reforestation. But thus far, IDRC project 3-P-72-0093 has barely tapped it. Second, this failure flows partially from poor performance in the project's research component. Technically feasible reforestation packages - species and planting techniques adapted to facilitate rapid wood production in the local sahelien environment - were to have been developed. To date they have not been. Selective protection of natural regeneration remains probably the most productive reforestation strategy. Peasants have known about this for years; many would like new information. Third, effective local participation in reforestation has been throttled by exclusive reliance in the IDRC project on a community woodlot system, ill-suited in the local socio-political context to serve as a vehicle for reforestation efforts powered and sustained by local people. Family woodlots offer more tangible incentives for participation and should be vigorously advocated."
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".