Canadian transit agencies response to COVID-19: Understanding strategies, information accessibility and the use of social media
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Over the past few months, transit agencies across Canada have been rushed to implement a range of strategies in response to the COVID-19 pandemic, with no standardized guidelines to direct their efforts. This study explores the initial response of transit agencies serving the 25 most populous Canadian cities by understanding the distinct types of response measures implemented between March 1st and June 1st, 2020. It also explores to what extent information related to these measures was accessible and usable, and how transit agencies used social media to communicate their efforts to the public. To achieve these goals, a detailed review of Canadian transit agencies websites and social media accounts was performed. The findings suggest that larger transit agencies across Canada implemented the most measures to respond to COVID-19, but not necessarily provided the most accessible information regarding the measures. Overall, while all transit agencies reduced the offered service's frequency and capacity and enhanced vehicle cleaning, the implementation of other physical and communication measures varied considerably between agencies. Information related to the number of COVID-19 cases within the workforce was least accessible across agencies. Transit agencies' Twitter platforms were used more by larger agencies. While most of transit agencies tend to employ tweets that include some type of graphics, very few agencies employed videos and animations to communicate important information to the public. This paper provides transit planners and policymakers with comprehensive information regarding the initial response of Canadian transit agencies to maintain operations in such critical times.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it