Who’s Online…and Who Isn’t?Equity Considerations for Web-Based Feedback and Transit
Bibliographic record
Abstract
The emergence of Web 2.0 technologies created new opportunities for transit agencies to interact with their riders and stakeholders. Web-based and mobile tools have offered new and powerful ways for transit properties to collect customer feedback while also empowering riders to use these tools to report issues from the field. TCRP Project B-43, Use of Web-based Customer Feedback to Improve Public Transit Services, is exploring the use of these new tools and developing a toolkit for transit operators. The study gathered information on current and planned use of web-based tools through an online survey administered to 130 transit agencies in the United States and Canada. Survey findings revealed that many transit operators did not have recent estimates of the proportion of riders with access to technology and some of the available estimates were lower than the national average. This raises a concern that many transit riders do not have access to web-based tools, creating a digital divide that could get wider as transit agencies increase their focus on web-based activities. At the same time, national trends in adoption of technology also suggest that some demographic groups have embraced technology and prefer to connect with transit agencies through web-based and mobile tools. To better understand these changes and to best meet the needs of their customers, transit agencies are encouraged to incorporate questions about access to technology into future rider surveys. This paper uses survey findings from Project B-43 but goes beyond the project scope to explore these issues.
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How this classification was reachedexpand
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 | 0.013 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".