Stakeholder Engagement and Public Information Through Social Media: A Study of Canadian and American Public Transportation Agencies
Bibliographic record
Abstract
This study uses theories on dialogic accounting to assess whether online interaction through social media is used as a mechanism of public information and stakeholder engagement by Canadian and American public transportation agencies. We embraced a quantitative methodology in which content analysis was performed on the Facebook and Twitter accounts of 35 transit operators in Canada and the United States. We categorized the contents of 1,222 Facebook posts and 2,615 tweets, assessed which level and what type of interaction was effectively reached for every category, tracked whether and how agencies reply to comments on their posts, and assessed the general tenor of the discussion. Our results show that public transportation agencies often take advantage of their presence on social media to provide the public with information on their services and to perform activities associated with stakeholder engagement. However, we have found some significant differences in the utilization of social media by public transportation agencies, all of which are discussed in the “Conclusion” section of this article. Twitter is most often used for public information messages, while Facebook appears to be used more to publish content in a dialogic perspective that creates two-way, collaborative conversations with users. In terms of practical implications, our study suggests that a broader and more continuous commitment to interaction between users and stakeholders on social media would create new opportunities for improving transparency and, indirectly, the services of public agencies.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.012 |
| Science and technology studies | 0.045 | 0.009 |
| Scholarly communication | 0.010 | 0.004 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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; 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".