ENGAGEMENT MATTERS: An exploration of public engagement and its futures in Toronto
Bibliographic record
Abstract
As public engagement gains momentum as a mechanism for engaging residents in a changing political climate, its effectiveness is more important than ever. This study, divided into three volumes, explores matters related to public engagement for city-building decisions. Volume 1 is a primer on public engagement, meant to support municipal public servants to understand the basics of public engagement. This Volume is informed by user and expert interviews, ethnographic observation, and system mapping tools conducted during the first phase of this research. Volume 2 builds on insights developed during the first phase of this research, to explore the futures of public engagement in 2033 using strategic foresight. Written for public servants familiar with the field of public engagement, Volume 2 is an exploration of key trends impacting the futures of public engagement and possible future scenarios. These scenarios were developed using a collective scenario process, which Volume 3 describes in detail, in the hopes that foresight and public engagement practitioners might find use in iterating and utilizing this process to explore participatory future-focused conversations.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.032 | 0.013 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".