Uses of Social Media in Public Transportation
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
This synthesis explores the use of social media among transit agencies and documents successful practices in the United States and Canada. Social media are defined as a group of web-based applications that encourage users to interact with one another, such as blogs, Facebook, LinkedIn, Twitter, YouTube, Flickr, Foursquare, and MySpace. Transit agencies have begun to adopt these networking tools to provide transit information as timely update, public service, citizen engagement, employee recognition, and entertainment. A review of the relevant literature was conducted. Because the field is new, there is not yet a large body of research available on social media. Relevant information was obtained from online sources, including blog posts, websites, conference presentations, online journals, and publications covering technology and governance. A selected survey of transportation providers in the United States and Canada known to use one or more social media platforms, and located in large metro, small urban, and rural areas, yielded a 90% response rate (34 of 39). Six transit providers participated in telephone interviews, highlighting more in-depth and additional details on successful practices, challenges, and lessons learned. These included providers in San Francisco, California; Dallas, Texas; Allentown, Pennsylvania; New York, New York; Morgantown, West Virginia; and Vancouver, British Columbia.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it