Tracking plan implementation using elected officials’ social media communications and votes
Bibliographic record
Abstract
Plans can only impact practice when elected officials adopt, enact, and approve funding for specific strategies. We explore ways to track implementation from the planning documents to elected officials’ priorities and to their voting patterns to identify the consistencies and gaps that may limit the impact of plans. We use Twitter data mining, text content analysis, and voting records from the digitized council minutes in Calgary, Alberta, between the 2017 municipal election and the last quarter of 2020. We connect the expressed preferences to votes for each councilor over the study period. On the two most salient topics—transit and affordable housing—those who expressed support on Twitter also supported investments. With one exception of an anti-tax councilor, over time, the rest of the councilors reached agreements on public investments (supra-local funding lightened the financial burdens for the city facilitating “yes” votes). Planners can derive meaningful information from the elected officials’ social media communication, such as concerns and support for specific planning initiatives, to promote successful plan implementation. This information can also enhance voters’ awareness of local officials’ views and actions on planning initiatives.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".