Destination Accessibility of Higher Educational Institutions from Scarborough
Bibliographic record
Abstract
The ability to access destinations such as educational institutions through public transportation can greatly benefit an individual in many aspects of life. Commuter students can face mobility issues when attending an urban university, issues that are not limited to mobility but also affect students’ academic success. This study presents an investigation of the destination accessibility of higher educational institutions in Toronto from Scarborough. The research emphasizes lived experiences collected through semi-structured interviews with post-secondary students who attend York University, Toronto Metropolitan University or the University of Toronto. Student experiences were analyzed to answer the research question: How does existing public transit affect Scarborough residents’ access to university education? The research found that mobility in Scarborough encouraged commuter students to manage their time in order to balance academics, commuting, and other responsibilities such as part-time jobs. The study revealed students’ appreciation for public transportation’s ability to get them to campus, but also captured their challenges surrounding the commute itself, which made being a student or attending campus difficult. Issues such as delays, busy transit, safety concerns, poor student mobility programs and a lack of information regarding transit schedules can also affect students’ academic success. The research also put forward three recommendations that can help improve students’ destination accessibility in the short and long term. The study is relevant as good access to higher education can be associated with increased income and improved life satisfaction, but poor destination accessibility can hinder one’s access to opportunities. The study is especially relevant in the context of Scarborough for two reasons. First, several transportation projects for the area have been cancelled. Second, statistics show that the area has a lower percentage of residents who have achieved a post-secondary education degree compared to most of Toronto.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".