Can’t Get No Satisfaction: Examining the Relationship between Commuting and Overall Life Satisfaction
Bibliographic record
Abstract
Commuting to work and school can be viewed as an unpleasant and necessary task.However, some people enjoy their commutes, and trip satisfaction can have a positive impact on overall life satisfaction.The purpose of this study is to analyze the relationship between individuals' satisfaction with their commuting trips and their reported overall life satisfaction.This study is based on the results of the 2015/2016 McGill Commuter Survey, a university-wide travel survey in which students, staff and faculty described their commuting experiences to McGill University, located in Montreal, Canada.Using a Factor-Cluster analysis, the study reveals that there is a relationship between trip satisfaction and the impact of commuting on overall life satisfaction.One result of the study shows that cyclists and pedestrians have the highest overall trip satisfaction, report that their life satisfaction is most impacted by their commute, and have the highest overall life satisfaction.Also, for all mode users, one or two clusters exhibit lower trip satisfaction, report that satisfaction with their commute does not greatly influence their life satisfaction, and claim having access to and using fewer modes relative to other users of the same mode.These results, in addition to the results that active mode users have high life and trip satisfaction, suggest that building well-connected multi-modal networks that incorporate active transportation can improve the travel experience of all commuters.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".