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Record W4206013204 · doi:10.36315/2021end019

HOW RELATIONSHIPS IMPACT SENSE OF BELONGING IN SCHOOLS AMONGST FEMALE ADOLESCENTS FROM REFUGEE BACKGROUNDS

2021· article· en· W4206013204 on OpenAlexaffabout
Sonja Aicha van der Putten

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsSimon Fraser University
Fundersnot available
KeywordsRefugeeFeelingSense of communityNarrativePsychologySet (abstract data type)Developmental psychologySocial psychologyGender studiesSociologyPolitical science

Abstract

fetched live from OpenAlex

Education is believed to play an essential role in creating a sense of belonging amongst adolescents from refugee backgrounds. This narrative inquiry study set out to better understand the influence that relationships formed in one Canadian school community played in the development of a sense of belonging amongst female adolescent students from refugee backgrounds. Study participants were from Middle Eastern and East African origin and had been living in Canada for two-years or less. Data were collected over a five-month period through two sets of interviews, and a series of observations. Findings indicated the students from refugee backgrounds sense of belonging in school was strengthened by strong relationships with teachers from whom they perceived a genuine sense of support and care, which resulted in higher academic achievement. The study also conveyed that students felt that their Canadian-born peers largely ignored them in class, which resulted in increased feelings of social isolation and lack of belonging. The female student experience was further influenced by additional familial obligations and responsibilities.

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)0.003
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 score0.072
Threshold uncertainty score0.143

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0070.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.001
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 head0.046
GPT teacher head0.337
Teacher spread0.291 · 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

Citations0
Published2021
Admission routes2
Has abstractyes

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