Navigating Civil War through Youth Migration, Education, and Family Separation
Bibliographic record
Abstract
Why did youth move from their trans-Himalayan villages at very young ages to attend school with the risk of prolonged family separation? An in-depth study of youth from rural trans-Himalayan villages who travelled to Kathmandu, capital of Nepal, to live and study at a (free) boarding school, funded by both national and international donors, provides a starting point to address this question. The “People’s War” from 1996 to 2006 in Nepal contextualizes the study, given that the Maoist insurgency in the Himalayan hinterland aimed to recruit youth to the rebel cause. The study of youth from the trans-Himalayan region living at the boarding school as students was conducted between April and July 2014 in Kathmandu. The youth arrived at the school between the ages of four and ten years, and did not see their families for several years after their arrival, given the significant distances between their villages and the associated costs of travel. Drawing on scholarship in children’s geographies, the narratives of these youth are employed to underscore their agency in these biographies of migration and better understand these difficult separations during political uncertainty and civil war.
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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 | 0.002 |
| 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 | 0.004 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".