MétaCan
Menu
Back to cohort
Record W4399728225 · doi:10.32920/26046565

Racialized Immigrant Youth in the Ontario Education System

2024· preprint· en· W4399728225 on OpenAlexaffabout
Liza Rodriguez

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldSocial Sciences
TopicEducation Systems and Policy
Canadian institutionsToronto Metropolitan UniversityYork University
Fundersnot available
KeywordsImmigrationPolitical scienceSociologyGender studiesDemographic economicsEconomics

Abstract

fetched live from OpenAlex

This study examines racialized immigrant youth within the Ontario education system, by answering the following research question: what are the experiences of racialized immigrant youth within the education system of Ontario? In answer to this question, a qualitative interpretative phenomenological research methodology was employed when conducting and analyzing in depth, semi structured interviews of 20 racialized immigrant youth ranging in age from 15-18, from a variety of backgrounds, who immigrated to Canada between 2015-2019. This study argues that cultural capital, social capital and discrimination and racism are all factors that hinder the success of racialized immigrant youth. It is through the welcoming and helpful attitudes of teachers and peers, having a thorough understanding of English and parental knowledge of the education system were all factors that helped racialized immigrant youth in the successful navigation of the education system. Finally, this paper suggests additional supports that are needed to help racialized immigrant youth and their families get an equitable education.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.044
Threshold uncertainty score0.188

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0150.003
Scholarly communication0.0030.001
Open science0.0010.004
Research integrity0.0010.001
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 head0.056
GPT teacher head0.377
Teacher spread0.320 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same topicEducation Systems and PolicyFrench-language works237,207