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Record W2963441508 · doi:10.1177/1745499919865140

A comparison of factors determining the transition to postsecondary education in Toronto and Chicago

2019· article· en· W2963441508 on OpenAlexaffabout
Karen Robson, Paul Anisef, Robert S. Brown, Jenny Nagaoka

Bibliographic record

VenueResearch in Comparative and International Education · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsRace (biology)PovertyIntersectionalitySociologyPostsecondary educationPoliticsLongitudinal studyHigher educationGender studiesPsychologyEconomic growthPolitical science

Abstract

fetched live from OpenAlex

We examine how race, sex and poverty contribute to the likelihood of attending two- and four-year colleges in Chicago and Toronto. In each city, we use longitudinal data on high school students and their postsecondary trajectories in order to explore how race and sex may impact differentially upon their educational pathways. Our analyses are informed by an intersectionality perspective, wherein we understand that life chances are shaped by the various traits and identities that individuals possess. In Toronto, Black males are less likely than all other groups to attend four-year colleges. We also find that two-year colleges appear to fulfill a different role in Toronto than they do in Chicago; that is, serving populations who may have been tracked into non-academic course selections in high school. We contextualize our findings within the very different political, cultural, and historical contexts of Ontario and Illinois.

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)0.000
metaresearch head score (Gemma)0.003
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.031
Threshold uncertainty score0.151

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.003
Science and technology studies0.0040.002
Scholarly communication0.0020.000
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.194
GPT teacher head0.597
Teacher spread0.403 · 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 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

Citations7
Published2019
Admission routes2
Has abstractyes

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