Road to Productivity: Analysis of Commuters’ Punctuality and Energy Levels at Work or School
Bibliographic record
Abstract
The strain of the daily commute can negatively impact performance at work. This study differentiates how various modes influence commuters’ punctuality and energy levels at work and school. The data for this study come from the 2013 McGill Commuter Survey, a university-wide survey in which students, staff and faculty described their typical commuting experience to McGill University, located in Montreal, Canada. Ten multilevel logistic regressions are used to determine the factors that impact 1) a commuter’s feeling of being energized when he or she arrives at work or school and 2) his or her punctuality. The authors' results show that weather conditions and mode of transportation have significant impacts on an individual’s energy at work and punctuality. The models indicate that drivers have the lowest odds of feeling energized, while bus users have the highest odds of arriving late for work. Cyclists, meanwhile, have the highest odds of feeling energized and being punctual. Overall, this study provides evidence that satisfaction with travel mode is associated with higher odds of feeling energized and being punctual. With these findings in mind, policy makers should consider developing strategies that aim to increase the mode satisfaction of commuters. Encouraging the habit of commuting by bicycle may also lead to improved performance at work or school.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".