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Muslim families’ understanding of, and reaction to, ‘the war on terror’.

2010· article· en· W2005710442 on OpenAlexaffabout
Cécile Rousseau, Uzma Jamil

Bibliographic record

VenueAmerican Journal of Orthopsychiatry · 2010
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsMcGill University
Fundersnot available
KeywordsFeelingAgency (philosophy)EmpowermentPsychologyIdentity (music)Learned helplessnessImmigrationDevelopmental psychologySocial psychologyGender studiesSociologyHistoryPolitical scienceAesthetics

Abstract

fetched live from OpenAlex

In multiethnic societies, the consequences of the war on terror (WOT) for Muslim youth are still not well understood and the school's role remains to be defined. This article documents the parent-child transmission of understanding and emotional reaction to the WOT in South Asian Muslim families in Montreal, Canada. For this qualitative study, the researchers interviewed 20 families. Results indicated that the families' emotional reactions and communication about these events were interlinked with family patterns of identity assignation. The majority of parents avoided talking with their children about the WOT and felt that these issues should not be discussed at school. Most children shared their parents' feelings of helplessness and familial patterns of identity assignation. Parents reporting a greater sense of agency displayed less avoidance, had a more complex vision of self and other, and favored the school's role in helping children make sense of these events. These results suggest that school interventions in neighborhoods strained by international tensions should emphasize immigrant parents' empowerment and provide spaces where their children feel comfortable expressing their concerns.

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)0.004
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 score0.229
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0060.005
Scholarly communication0.0020.001
Open science0.0010.002
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 head0.019
GPT teacher head0.312
Teacher spread0.293 · 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

Citations52
Published2010
Admission routes2
Has abstractyes

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Same venueAmerican Journal of OrthopsychiatrySame topicMigration, Health and TraumaFrench-language works237,207