News and Social Media as Performance Indicators for Public Involvement in Transportation Planning: Eglinton Crosstown Project in Toronto, Canada
Bibliographic record
Abstract
A variety of communication channels and outreach tools are in use by Public Involvement (PI) programs in transportation planning and construction. Several metrics and measures are traditionally used to evaluate the performance of such programs and to measure their level of success in achieving their pre-defined goals. These metrics are mainly quantitative, attempting to connect the success of PI to the outreach size. However, the prevalence of modern techniques, particularly tools offered by Social Web in PI practices, is adding a new dimension to the definition of success for such programs. Evaluating such practices calls for a better understanding of the and meaning of communicated content rather than quantity of participants or communication installments. Based on this philosophy, the current paper introduces computational linguistic and semantic analysis as methods to process the content and crystallize the core topics discussed. Methods benchmarked from information retrieval are used to make sense of the content of public meetings discussions. Two parallel resources including online social media (Twitter), and news (online and offline newspapers) are then used to enhance the evaluation process. The Eglinton Crosstown transit project in Toronto is used as a case study in this paper. The project presents an interesting case in which a major design change was made due to public consultations. The objective of this paper is to use results from the analysis of content to highlight trends that may have been among factors causing the decision change. By focusing on changes in technical and social aspects of discussions before and after the decision change, the authors investigate how the changes relate to public needs announced through multiple channels.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".