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Record W4387601945 · doi:10.1080/13603116.2023.2263014

Canadian Nova Scotian Black learners in the Individualised Program Plan (IPP): intersectionality analysis and findings from a household survey

2023· article· en· W4387601945 on OpenAlexafffundabout
George Frempong, Raavee Kadam, Joyline Makani, Michelle McPherson, Nyasha Patience Mandeya, Timi Idris

Bibliographic record

VenueInternational Journal of Inclusive Education · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicDisability Education and Employment
Canadian institutionsAtlantic School of TheologySaint Mary's UniversityDalhousie University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsNova scotiaIntersectionalityCurriculumSociologyPedagogyPolitical scienceInclusion (mineral)Socioeconomic statusNova (rocket)Gender studiesPublic relationsMedical education

Abstract

fetched live from OpenAlex

In the Canadian Nova Scotian education system, the Individualised Program Plan (IPP) is designed to support students for whom the public school programme curriculum outcomes are not applicable or attainable. Schools can also place students in IPP programmes based on their behaviour. For minority students, especially Blacks, evaluation reports indicate their over-representation in these programmes and, therefore, the need for this research. Through intersectionality analysis of a household survey, our study explores how students’ multiple identities impact their designation as IPP. Our analysis indicated that schools tend to place Black male/female students from non-immigrant households with low socioeconomic backgrounds in IPP programmes making students with these multiple identities the most vulnerable. We argue for an intersectionality framework to address this challenge and inform the implementation of the current Nova Scotia inclusive education policy.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.258
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.069
GPT teacher head0.400
Teacher spread0.332 · 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 designObservational
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

Citations1
Published2023
Admission routes3
Has abstractyes

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