Conflict induced internal displacement in Nepal
Bibliographic record
Abstract
Nepal has witnessed a humanitarian crisis since the Maoist conflict began ten years ago. The plight of internally displaced persons (IDPs) in Nepal has received little international attention despite being rated one of the worst displacement scenarios in the world. An estimated 200,000 people have been displaced as a result of the conflict, with the far-western districts of Nepal being the worst affected. Internal displacement has stretched the carrying capacity of several cities with adverse physical and mental health consequences for the displaced. Vulnerable women and children have been the worst affected. The government has adopted a discriminatory approach and failed to fulfil its obligations towards IDPs. Non-governmental organisations and international agencies have provided inadequate services to IDPs in their programmes. Tackling the issues of IDPs requires co-operation between government and development agencies: acknowledging the burden of the problem of IDPs, adequate registration and needs assessment, along with health and nutritional surveys, and development of short-term emergency relief packages and long-term programmes for their assistance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 teacher head, 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".