Forest Resource Management and Utilisation through a Gendered Lens in Namibia
Bibliographic record
Abstract
The shift in forestry policy towards resource management and access rights from state control to local community control has been a welcome step towards sustainable forest management in Namibia. The policy acknowledges the direct dependence on natural environmental resources by the proportional majority of the population that live in the rural areas of Namibia. This study was aimed at performing gender analysis by identifying relationships of various groups to natural resources. The study further assessed the influence these relationships have on control, access and use of forest resources, as well as on natural resource management and the implications thereof on various forest management efforts in the country. Data were collected from seven community forest institutions in Namibia and analysed using the Harvard Gender Analytical Framework. The findings show a gendered differentiated knowledge, control and access to forest resources and unequal participation in leadership and governance. Furthermore, the results suggest that unequal power relations among minority and vulnerable groups affect access to and control of forest resources. This study proposes participation of both men and women in the management, protection, access and utilisation of forest resources, as this will contribute to sustainable forest management and economic development of all members of society.
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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 | 0.002 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".