Growing conversation : understanding planning literacy in the city of Toronto
Bibliographic record
Abstract
The City of Toronto’s commitment to meaningful public engagement in planning processes is apparent throughout its many policies, plans and initiatives. However, despite consistent messaging that effective engagement leads to better outcomes and the many hours it spends on engagement efforts, the City Planning division has acknowledged that its approaches to community outreach are not always effective. City Planning’s 2014 “Growing Conversations” initiative revealed, among other findings, disparities in levels of planning knowledge between experts and stakeholders that lead to frustration and communication breakdowns. An outcome of this was to focus on improving the planning literacy levels of Torontonians. However, Growing Conversations did not elaborate on how it intends to improve planning literacy, nor what it understands planning literacy to mean. By exploring the meaning of “planning literacy” and creating a diagnostic tool to measure it, this research paper seeks to stimulate meaningful discussion around what planning concepts everyday Toronto residents should be familiar with, help identify those concepts for which that is not the case, and consider how to act on that information. Key words An article on urban planning in the City of Toronto, used the key words: planning literacy; public engagement; questionnaire; Toronto.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".