The Intersectionality of Postsecondary Pathways: The Case of High School Students with Special Education Needs
Bibliographic record
Abstract
Utilisant des données du Toronto District School Board, cette étude examine les parcours postsecondaires des étudiants qui ont besoin de services d’éducation de l'enfance en difficulté. Nous observons les parcours qui mènent au collège ainsi que ceux menant à l'université et nous nous servons de régression logistique multinomiale à plusieurs niveaux afin de situer nos résultats dans le contexte du cycle de vie. Nos résultants démontrent que les étudiants qui ont besoin de services d'éducation de l'enfance en difficulté ont moins de chances d'accepter une offre d'une université mais plus de chances d'accepter une offre d'un collège. Nous analysons un groupe de facteurs connus et bien documentés qui influencent le parcours postsecondaire afin de connaître leur rôle quant au parcours des étudiants qui ont besoin de services d'éducation de l'enfance en difficulté. Nos résultats démontrent que le niveau d'éducation des parents, le revenu du quartier, la race et la filière scolaire ont tous un impact sur le parcours postsecondaire de cette population étudiante. Using data from the Toronto District School Board, we examine the postsecondary pathways of students with special education needs (SEN). We consider both university and college pathways, employing multilevel multinomial logistic regressions, conceptualizing our findings within a life course and intersectionality framework. Our findings reveal that having SEN reduces the likelihood of confirming university, but increases the likelihood of college confirmation. We examine a set of known determinants of postsecondary education (PSE) pathways that were derived from the literature and employ exploratory statistical interactions to examine if the intersection of various traits differentially impacts upon the PSE trajectories of students with SEN. Our findings reveal that parental education, neighborhood wealth, race, and streaming impact on the postsecondary pathways of students with SEN in Toronto.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".