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Record W2413158125 · doi:10.1080/15595692.2015.1137282

Learning and Teaching With Loss: Meeting the Needs of Refugee Children Through Narrative Inquiry

2016· article· en· W2413158125 on OpenAlexaff
Thursica Kovinthan

Bibliographic record

VenueDiaspora Indigenous and Minority Education · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and experiences of immigrants and refugees
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsRefugeeNarrativePerspective (graphical)PedagogyMulticulturalismPsychosocialNarrative inquiryPsychologyMulticultural educationQualitative researchSociologyMathematics educationPolitical scienceSocial science

Abstract

fetched live from OpenAlex

Providing refugee students with a safe and welcoming classroom environment is critical for school success but largely dependent on teachers’ knowledge, values, practices, and attitudes. This qualitative study juxtaposes the experience of one refugee students’ experience in the school system and one beginning teachers’ experience in working with and meeting the psychosocial and educational needs of refugee students in the classroom. Using narrative inquiry, from the perspective of a refugee student and a beginning teacher, this study identifies themes and key issues related to teaching refugee students. These experiences are compared to the current literature on refugee education to highlight the beliefs and values that teachers bring to their practice. Findings reveal that there are gaps in beginning teachers’ knowledge about who refugees are, their experiences, and how best to support them in the classroom. Some teachers also held negative attitudes and perspective of refugee students and failed to develop a nuanced perspective of diversity and multiculturalism. This study also shows how narrative inquiry, in the formal of a personal history account, can be used as tool to surface, challenge, and overcome negative stereotypes, biases, and assertions that prevent teachers from effectively supporting their students.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.171
Threshold uncertainty score1.000

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 studies0.0020.001
Scholarly communication0.0000.000
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 head0.011
GPT teacher head0.304
Teacher spread0.294 · 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.

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
Published2016
Admission routes1
Has abstractyes

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