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Record W2810781583 · doi:10.7202/1048575ar

Equal Education, Unequal Jobs: College and University Students with Disabilities

2018· article· en· W2810781583 on OpenAlexaffvenueabout
Jennifer M. Stewart, Saul Schwartz

Bibliographic record

VenueRelations industrielles · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLabor market dynamics and wage inequality
Canadian institutionsCarleton University
Fundersnot available
KeywordsDisadvantageGraduation (instrument)Propensity score matchingDescriptive statisticsEducational attainmentMatching (statistics)Drop outSpecial educationPostsecondary educationPsychologyHigher educationMedical educationLearning disabilityDemographic economicsActuarial scienceMathematics educationEconomicsMedicinePolitical scienceEconomic growthStatisticsDevelopmental psychology

Abstract

fetched live from OpenAlex

Are students with a permanent disability more likely to drop out of post-secondary education than students without a permanent disability? Once they are out of postsecondary education, do their experiences in the labour market differ? Answers to these questions are necessary to evaluate current policies and to develop new policies. This paper addresses these two questions using a unique data set that combines administrative records from the Canada Student Loans Program with survey responses. Our measure of permanent disability is an objective one that requires a physician’s diagnosis. The survey data supply information on the students’ education and labour market status. Simple descriptive statistics suggest that, compared to students without a permanent disability, students with a permanent disability are equally likely to drop out of postsecondary education, but less likely to be in the labour force and more likely to be unemployed. We use propensity score matching to address potential selection into the group of students who documented their disability. The results using propensity score matching are consistent with the descriptive statistics. Our story is one of an underpublicized success—the rising number of students with disabilities in postsecondary institutions and their equal likelihood of graduation—and a persistent problem—the continued disadvantage that people with disabilities, even those with the same educational attainment as people without disabilities, face in the labour market.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.385
Threshold uncertainty score0.462

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.027
GPT teacher head0.237
Teacher spread0.210 · 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

Citations12
Published2018
Admission routes3
Has abstractyes

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