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Record W2127909573 · doi:10.1111/chso.12131

Immigrant Children: Their Experience of Violence at School and Community in Host Country

2015· article· en· W2127909573 on OpenAlexafffundabout
Louise Hamelin Brabant, Simon Lapierre, Dominique Damant, Mélissa Dubé‐Quenum, Geneviève Lessard, Claudia Fournier

Bibliographic record

VenueChildren & Society · 2015
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsUniversité de MontréalUniversity of OttawaUniversité Laval
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsImmigrationCoping (psychology)Qualitative researchNarrativeSociologySuicide preventionPoison controlPsychologyGender studiesMedicinePolitical scienceClinical psychologySocial scienceEnvironmental health

Abstract

fetched live from OpenAlex

This article describes our qualitative sociological study of immigrant children's life experiences of violence. We conducted interviews with 42 first‐generation immigrant children from any country, aged 9–13 years old, living in the Quebec City region (Canada). Results from three main themes are presented: representations of violence and concrete violent acts experienced; perceived effects of violence on children health and well‐being; and reactions and coping strategies. Overall, the narratives show that they may experience racist peer violence in school that leads to suffering situations, and they consequently have to develop strategies to maintain their well‐being. Social implications are discussed.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.138
Threshold uncertainty score0.275

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0100.005
Scholarly communication0.0030.001
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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 head0.021
GPT teacher head0.296
Teacher spread0.275 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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

Citations22
Published2015
Admission routes3
Has abstractyes

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