Talking over the waterfront: A qualitative study of Waterfront Toronto’s public engagement practice
Bibliographic record
Abstract
This is a qualitative study of Waterfront Toronto’s (WT) public consultation practice, a hallmark of its work leading the redevelopment of Toronto’s urban waterfront, since 2001. Motivated in part by the absence of similarly focused studies on one of the longest and most sustained public engagement efforts by a public agency in Canada. Data was gathered by interviewing a purposive sample of key informants all of whom participants in, or close observers of WT’s public engagement practice. This is an appreciative though not uncritical ‘insider’ study, one that accepts its adjacency to the research topic. The key informants offered their views on what worked, what was missed and could be improved in the conduct of WT’s public consultation practice. The predominant view being that WT has set the gold standard for public consultation and could serve as model for public agencies to emulate. The key caveat to this positive finding was the issue of adequate inclusion: a question of who is or is not ‘in the room’. It is recommended that deficits of inclusion by remedied by proactive meliorative approach to enhancing outreach, while recognizing the limits of consultation, which is seen as a supplement to, not a replacement for representative democracy.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.025 | 0.020 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 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".