Sustaining Rural Afghanistan under Limited Central Government Influence
Bibliographic record
Abstract
Land and water access insecurity, land grabbing, and unstable common property status of critical local resources continue to drive conflicts, rural landlessness and environmental problems throughout many areas of Afghanistan where formal government is weak or entirely absent. In contrast to traditional development strategies that favor infrastructure enhancement and backed by enforced national policies, we offer Afghan-specific strategies based on resource conservation and increased capacity of local resource management institutions that can function when and where central government cannot be relied upon to assume or maintain a supportive role. Resource conservation and building local capacity are key components of existing and proposed future efforts to increase stability. However, support for these efforts, whether government or community-based, has been limited in portions of rural Afghanistan , apparently due to low stakeholder confidence in retaining access to improved land, water and other critical resources when international forces withdraw. Powerful individuals and groups, operating outside local community structures, are increasingly impacting land use practices. We suggest a thorough assessment of the present and likely future social environment, including awareness of likely conflicts resulting from agricultural or natural resource improvements, before any tangible actions are taken.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".