What physical exposures to risks can post-secondary students encounter when taking public transit trips in Toronto?
Bibliographic record
Abstract
Post-secondary students are more likely to use public transit than other demographic groups. As a result of dangerous occurrences, they are seen as a specific demographic group that may become victims of crime because of public transit trips. This paper depicts the research that will allow us to take a deeper look at how students integrate public transit trips into their lives and better understand the possible dangers they face in Toronto. The StudentMoveTO 2019 survey contains 18,513 post-secondary student responses, including trips taken where students used public transit to travel to a destination in Toronto. It also uses 2019 crime and injury variables including Assaults, Robberies, Shootings, and Traffic Accidents and socio-economic and demographic variables. Spatial autocorrelation techniques were used to examine spatial autocorrelation and hotspots for public transit trip and risk locations. An Ordinary Least Square (OLS) regression and a Geographically Weighted Regression (GWR) were performed to assess risk and public transit trip hotspots and were used to determine how socio-economic and demographic variables, in combination with the assumed spatial risk of crime and injury, are connected to public transit trips. The GWR indicated that students are more likely to be robbed since it had the greatest R2 value of 0.844 whereas the OLS regression had a lower R2 value of 0.616. According to the findings, students who took public transit trips are more vulnerable when they travel in the evening, particularly in the northern section of North York and Downtown, Toronto. Using the discovered results, it is possible to raise awareness among post-secondary students who take public transit trips by informing them about less secure census tracts.
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 | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".