Bibliographic record
Abstract
“Steps in reserve” are the steps that people could take but do not take because they choose to travel by using a motorized mode for short trips instead of walking. Many studies have already confirmed that the potential to walk is important and that a shift from motorized mode to walking for short trips could be beneficial to many people from a physical activity perspective. The current research is an extension of this study of latent walk trips and proposes a comparison of steps in reserve in two large Canadian metropolitan areas—Montreal, Quebec, and Toronto, Ontario—for various population segments and neighborhood densities. It also adds measures of how these steps in reserve have evolved over time and discusses whether current travel behaviors have been influenced by ever-increasing promotional campaigns to be more active. The research relies on rich sources of microdata on daily travel behaviors; data from four large-scale origin–destination travel surveys (1998 and 2003 for the Montreal area and 1996 and 2001 for the Toronto area) are processed in order to estimate the impacts of a theoretical mode shift scenario on the number of steps people could add to their volume of physical activity on a daily basis. Results show that steps in reserve are quite consistent across metropolitan areas. Around 15% of the population have steps in reserve, an average of 2,430 steps per day. Moreover, the estimated models confirm that both the propensity to have steps in reserve and the number of steps in reserve vary according to individual, household, and neighborhood features. For instance, living alone and owning a driver's license will increase the probability of having steps in reserve, whereas being a full-time worker decreases the average number of steps in reserve per day.
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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 | 0.000 | 0.002 |
| 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 | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.060 | 0.012 |
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".