MétaCan
Menu
Back to cohort
Record W3003363936 · doi:10.14507/epaa.28.4200

Redefining risk: Human rights and elementary school factors predicting post-secondary access

2020· article· en· W3003363936 on OpenAlexaffabout
Robert S. Brown, Kelly Gallagher‐Mackay, Gillian Parekh

Bibliographic record

VenueEducation Policy Analysis Archives · 2020
Typearticle
Languageen
FieldSocial Sciences
TopicYouth Substance Use and School Attendance
Canadian institutionsYork UniversityToronto Metropolitan University
Fundersnot available
KeywordsScrutinyLegislationPsychologyWarrantSet (abstract data type)Race (biology)At-risk studentsPerspective (graphical)Academic achievementMedical educationMathematics educationPedagogyPolitical scienceSociologyMedicineBusiness

Abstract

fetched live from OpenAlex

While there is a widespread consensus that students’ pathways towards postsecondary education are influenced early in life, there is little research on the elementary school factors that shape them. Identifying educational ‘risk factors’ directs attention to barriers that may warrant scrutiny or action under human rights legislation. New findings from a unique, longitudinal data set collected and developed by the Toronto District School Board highlights key factors, established in elementary school, as to how many students do not enter into post-secondary studies in Ontario. The majority of students suspended at any time, students in self-contained special education programs, and/or students who missed more than 10% of classes in grade 4 do not go on to PSE. These organizational factors are more predictive of students’ acceptance to PSE than individualized measures of preschool readiness, academic achievement in grade 3, race or parental education. These structural ‘risks’ are strongly correlated with of race and disability. In light of research that identifies promising, evidence-based practices available to reduce these risks, breaking down these barriers should be a priority from the perspective of improving PSE access and overcoming what may well amount to systemic discrimination.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0020.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.030
GPT teacher head0.356
Teacher spread0.326 · 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.

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

Citations6
Published2020
Admission routes2
Has abstractyes

Explore more

Same venueEducation Policy Analysis ArchivesSame topicYouth Substance Use and School AttendanceFrench-language works237,207