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Record W3195569378 · doi:10.32920/ryerson.14645145.v1

Ontario Safe Schools Act and its Effects on Racialized Immigrant Youth: 'School to Prison Pipeline'

2021· preprint· en· W3195569378 on OpenAlexaffabout
Sofiya Kovalenko

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPsychology
TopicMigration, Health and Trauma
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsRacializationCriminalizationImmigrationCriminologyDisciplineSchool disciplinePrisonCriminal justicePopulationSociologyPolitical scienceGender studiesRace (biology)LawDemographyPedagogySocial science

Abstract

fetched live from OpenAlex

It is recognized that racialized youth are significantly over-represented in the Canadian Criminal Justice System relative to their population percentages. Research also determined that similar disproportion exists with respect to school discipline. Similar to US research, a number of Canadian studies found that racialized youth are being disproportionately affected by zero-tolerance school disciplinary policies, such as the Ontario Safe Schools Act. Such research also hypothesized about a "school-to-prison pipeline" for minority youth. This MRP explores the link between immigration, policing, and school disciplinary policies in Ontario, Canada. In particular, the MRP investigates the racialization of school disciplinary procedures that largely affect immigrant youth, and the criminalization of certain behaviors that may lead visible minority youth, including immigrant youth, to having disproportionate police contact. The findings suggest that there is a relation between racial disproportion of school suspensions and expulsions and the racial disproportion in the likelihood of youth- police contact.

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)0.005
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.034
Threshold uncertainty score0.247

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.005
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0080.002
Scholarly communication0.0020.001
Open science0.0010.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0090.000

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 head0.032
GPT teacher head0.333
Teacher spread0.302 · 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
Published2021
Admission routes2
Has abstractyes

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