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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".