Redefining risk: Human rights and elementary school factors predicting post-secondary access
Bibliographic record
Abstract
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 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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".