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Record W2188592739 · doi:10.25071/1920-7336.40311

Navigating Civil War through Youth Migration, Education, and Family Separation

2015· article· en· W2188592739 on OpenAlexaffvenue
Adrian A. Khan, Jennifer Hyndman

Bibliographic record

VenueRefuge Canada s Journal on Refuge · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicChildren's Rights and Participation
Canadian institutionsYork UniversityUniversity of Toronto
Fundersnot available
KeywordsSpanish Civil WarScholarshipAgency (philosophy)Capital cityNarrativeBoarding schoolInsurgencyPoliticsGeographyPolitical scienceGender studiesCapital (architecture)SocioeconomicsSociologySocial scienceIslam

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score0.955

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.042
GPT teacher head0.353
Teacher spread0.311 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations8
Published2015
Admission routes2
Has abstractyes

Explore more

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