Connecting Crises: Young People in Nepal Reflecting on Life Course Transitions and Trajectories during Times of Uncertainty
Bibliographic record
Abstract
During certain crises, displacement of populations seeking safe refuge elsewhere can occur without the certainty of a return, if at all. Children and young people in such contexts often face the additional challenge of restrictions or disregard towards engaging their agency in migration decision-making processes. Through 60 in-depth interviews with 30 trans-Himalayan participants (ages of 16–23) and multi-sited ethnography throughout Nepal, this paper investigates multiple experiences of crises experienced by young people and the effects on their life course trajectories. From focusing on the Civil War in 1996–2006, the 2015 earthquake, and most recently the COVID-19 pandemic, this paper proposes that initial displacements from the Civil War, when connected with other crises later on in a participant’s life course, better prepared them to deal with crises and enabled them to create a landscape of resilience. Furthermore, a landscape of resilience that connects past and present life course experiences during crises prepared some participants for helping their larger communities alleviate certain crises-related tension. Overall, this paper extends analysis on an under-researched group of young migrants by connecting crises that shaped their (im)mobility and life trajectories, rather than approaching crises as singular, isolated experiences.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 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".