On the Factors Influencing the Choices of Weekly Telecommuting\n Frequencies of Post-secondary Students in Toronto
Bibliographic record
Abstract
The paper presents an empirical investigation of telecommuting frequency\nchoices by post-secondary students in Toronto. It uses a dataset collected\nthrough a large-scale travel survey conducted on post-secondary students of\nfour major universities in Toronto and it employs multiple alternative\neconometric modelling techniques for the empirical investigation. Results\ncontribute on two fronts. Firstly, it presents empirical investigations of\nfactors affecting telecommuting frequency choices of post-secondary students\nthat are rare in literature. Secondly, it identifies better a performing\neconometric modelling technique for modelling telecommuting frequency choices.\nEmpirical investigation clearly reveals that telecommuting for school related\nactivities is prevalent among post-secondary students in Toronto. Around 80\npercent of 0.18 million of the post-secondary students of the region, who make\nroughly 36,000 trips per day, also telecommute at least once a week.\nConsidering that large numbers of students need to spend a long time travelling\nfrom home to campus with around 33 percent spending more than two hours a day\non travelling, telecommuting has potential to enhance their quality of life.\nEmpirical investigations reveal that car ownership and living farther from the\ncampus have similar positive effects on the choice of higher frequency of\ntelecommuting. Students who use a bicycle for regular travel are least likely\nto telecommute, compared to those using transit or a private car.\n
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".