From advocacy to acceptance: Social media discussions of protected bike lane installations
Bibliographic record
Abstract
Many North American cities are building bicycling infrastructure. Lively discussions on social media, where people passionately support or reject bicycling infrastructure projects, provide a unique data set on attitudes towards bicycling infrastructure. Our goal is to analyse social media posts in Edmonton and Victoria, Canada as new bike infrastructure was implemented to understand the thematic and social elements of the conversation and how these changed over time. We collected Twitter messages ( n = 13,121: 7640 in Edmonton; 5481 in Victoria) and compared three timeframes: before lanes opened (January 2015 to lane opening); the first riding season (opening to April 2017); and the second riding season (May 2017 to November 2018). For each timeframe, we evaluated word-combination frequencies (to understand the use of language) and social network structures (to understand which accounts were influential and how they interacted). We observed a change in the three time periods. Before the bicycling infrastructure was built, Twitter activity was focused on advocacy, which was especially strong in Victoria. The first riding season had the most social media activity, the most diverse perspectives and the most controversy. The second riding season held more support. Based on the Twitter activity, we found that Edmonton had more support from local businesses and traditional media, launching a connected network of infrastructure with less social media opposition. Our results suggest that attitudes associated with change in bicycling infrastructure may have a cycle, with initial negative responses to change, followed by an uptick in positive attitudes.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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; a candidate call from one teacher head, 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".