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Record W3195798258 · doi:10.11575/prism/37387

Investigating Migration through the Phenomenon of School Integration: Anaya’s Experience of Resettlement in Canada

2019· article· en· W3195798258 on OpenAlexaboutno aff
Anusha Kassan, Alissa Priolo, Suzanne Goopy, Nancy Arthur

Bibliographic record

VenuePRISM (University of Calgary) · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicCritical Race Theory in Education
Canadian institutionsnot available
Fundersnot available
KeywordsPhenomenonImmigrationDemographic economicsPolitical scienceSociologyEconomic growthPsychologyEconomicsEpistemology

Abstract

fetched live from OpenAlex

Using a social justice framework, this arts-based engagement ethnography (ABEE) investigated the phenomenon of school integration among newcomer youth who migrated to Canada. Defined broadly, this phenomenon captures the adjustment of newcomer youth across all aspects of student life – both inside and outside the school context, including English Language Leaning (ELL), academic performance, classroom behaviour, social networking, emotional and familial well-being, involvement in school life, and understanding of the educational system. Specifically, two research questions were investigated: 1) How do newcomer youth experience school? and 2) How do these experiences influence their positive integration into the school system? Results from one participant – Anaya, a 19-year-old cisgender female who migrated to Canada from India with her family at the age of 12 – are presented to illustrate the manner in which the phenomenon of school integration can be used as a point of entry to study migration. These result included the following five themes: 1) The Struggle to Fit In / “I regard myself as a social outsider”, 2) Managing Parental Expectations / “Our values started to clash”, 3) Implications of Self-Exploration / “I was kind of in the middle”; 4) Finding a Passion and Getting Involved / “I became a lot more friendly”, and 4) Embracing a Multicultural Identity / “I am reembracing my heritage.”

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation 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 score0.222
Threshold uncertainty score0.447

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.022
GPT teacher head0.280
Teacher spread0.258 · 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.

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

Citations5
Published2019
Admission routes1
Has abstractyes

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