Influence of latent attitudinal factors on the multimodality of post-secondary students in Toronto
Bibliographic record
Abstract
The study explores the causal relationships between latent factors and various observed external variables along with their ability to explain multimodal behaviours of post-secondary students in Toronto. Multimodality was measured by the number of unique modes used by any individual for their daily travels. As opposed to using a single mode of transportation, use of multiple modes for different trips indicates the degree and the nature of multimodality. For the empirical investigation, the study uses structural equation modelling and ordered probability modelling for a dataset collected through a large-scale travel diary survey among four major universities in Toronto representing over 180 000 post-secondary students in the region. The results of the empirical investigation reveal that latent attitudes are influential factors in determining the multimodal behaviour of post-secondary students in Toronto. The results also found that mobility tool ownership and land use characteristics have a significant influence on those latent attitudes, and are direct determinants of the degree of multimodality. In particular, the results indicate that smart fare payment cards have a considerable effect on latent attitudes for post-secondary students. These findings could have policy implication from the planning perspective and should warrant further investigations.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".