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Record W3206599581 · doi:10.46303/jcve.2021.6

The construction of the racialized Other in the educational sphere: The stories of students with immigrant backgrounds in Montréal

2021· article· en· W3206599581 on OpenAlexaffabout
Fahimeh Darchinian, Marie‐Odile Magnan, Roberta Soares

Bibliographic record

VenueJournal of Culture and Values in Education · 2021
Typearticle
Languageen
FieldSocial Sciences
TopicEducation, sociology, and vocational training
Canadian institutionsUniversité de Montréal
Fundersnot available
KeywordsRacializationSociologyGender studiesSocial psychologyPsychologyRace (biology)

Abstract

fetched live from OpenAlex

This paper presents the results of an empirical study of social relations from a critical race theory perspective crossed with the sociology of the life course. The objective of our study was to understand how social relations in Quebec’s educational sphere, specifically in high school, construct fixed categories of racialized students in university. With the aim of discovering the underlying process of racialization of the students of racial backgrounds in educative sphere, the study analyzes the self-reported relational experiences of 10 university students with immigrant backgrounds in Montréal. Based on a narrative inquiry, the analysis of the retrospective life story interviews allowed to explain the complexity of the process of racialization in two categories of “complete racialization” and “incomplete racialization.” In the “completed racialization” category, negotiating domination relationships results in the construction of a racialized Other. In the “incomplete racialization” category, the construction process is in progress. Our study has shown that social relations in high school contribute to the construction of fixed Black and Latinx racialized groups. Interpersonal relationships at school play a role in the racialization of students with immigrant backgrounds, and, although limited in scope, persistence in school may be a reversal strategy for their experiences of racism.

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)0.003
metaresearch head score (Gemma)0.001
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 score0.161
Threshold uncertainty score0.585

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.047
GPT teacher head0.427
Teacher spread0.380 · 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

Citations4
Published2021
Admission routes2
Has abstractyes

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