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Record W4391658940 · doi:10.32920/25193462

An Ecological Approach to Refugee Youth Identity Development in Small-Sized Cities

2024· preprint· en· W4391658940 on OpenAlexaffabout
Carly McFall

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan UniversityUniversity of Manitoba
Fundersnot available
KeywordsRefugeeThematic analysisContext (archaeology)Identity (music)Exploratory researchPositive Youth DevelopmentPerceptionSocial identity theoryQualitative researchSociologyGender studiesPolitical sciencePsychologySocial psychologyDevelopmental psychologyGeographySocial groupSocial science

Abstract

fetched live from OpenAlex

Refugee youth experience resettlement during a critical developmental period. Identity development is a key task of adolescence that is impacted by interactions held in certain places. Currently there is a gap in the literature exploring refugee youth identity development, particularly within the context of small-sized cities. The aim of the current exploratory qualitative research study was to develop a better understanding of how interactions within different systems influence refugee youth perceptions of their identity in the context of a small- sized Canadian city. Two refugee youth from two small-sized cities were asked to engage in an online interview to discuss how interactions with family, friends, school, social media, and community and religious organizations may influence their identities. The results from the thematic analysis indicated that resettlement challenges, changing roles and responsibilities, supportive interactions, negative interactions, communication and social connections, and age at the time of resettlement influenced refugee youth differentially.

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)0.005
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.481
Threshold uncertainty score0.956

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0040.004
Science and technology studies0.0210.011
Scholarly communication0.0050.003
Open science0.0020.009
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.000

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 head0.096
GPT teacher head0.366
Teacher spread0.271 · 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 designObservational
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
Published2024
Admission routes2
Has abstractyes

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