MétaCan
Menu
Back to cohort
Record W4245216582 · doi:10.47678/cjhe.v48i1.187972

Underrepresented Students and the Transition to Postsecondary Education: Comparing Two Toronto Cohorts

2018· article· en· W4245216582 on OpenAlexaffvenueabout
Karen Robson, Paul Anisef, Robert S. Brown, Rhonda C. George

Bibliographic record

VenueCanadian Journal of Higher Education · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicHigher Education Research Studies
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsHigher educationDemographyMultinomial logistic regressionPostsecondary educationPsychologyLogistic regressionAffect (linguistics)SociologyPolitical scienceMedicine

Abstract

fetched live from OpenAlex

Using data from two cohorts of Grade 12 students in Toronto, we examined whether the transition to post-secondary education changed between 2006 and 2011, particularly for under-represented groups. We used multilevel, multinomial logistic regressions to examine how the intersections of race and sex affect post-secondary transitions in the two cohorts. Our findings revealed that Black, Latino, and Southeast Asian students were less prepared for post-secondary education than White students. Students in these groups had lower than average GPAs, higher identification of special education needs, or lower likelihoods of taking academic-stream courses. These differences remained fairly stable between 2006 and 2011. We did, however, find that Black students were more likely than White students to confirm a place in university in 2011—a significant difference. In contrast, Southeast Asian students experienced a decline in university transition but an increase in college confirmation. We also found that race and sex were important intersections for university confirmations in the case of Blacks and for college confirmations in the case of Southeast Asians. We contextualize our findings within the policy climate of Ontario in the years spanning our two cohorts.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.271
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0000.000
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 head0.028
GPT teacher head0.417
Teacher spread0.389 · 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 designNot applicable
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

Citations30
Published2018
Admission routes3
Has abstractyes

Explore more

Same venueCanadian Journal of Higher EducationSame topicHigher Education Research StudiesFrench-language works237,207