Meaningful and inclusive engagement: are we there yet? A case study of Scarborough neighbourhood improvement areas (NIAS) participatory processes
Bibliographic record
Abstract
While Western planning shows an awareness about the importance of citizen participation, it is acknowledged that the conventional public meeting approach fails to truly engage with the public. Moreover, studies reveal this approach’s limitations particularly impact ethnic communities. Research has been limited to investigate the inefficiency of the participatory framework from the experts’ perspective. It has not addressed the issue of the lack of ethnic diversity in participatory processes nor have made concrete suggestions of changes for policymakers. This research investigates how ethnically diverse citizens participate and are engaged with the municipal participatory framework to uncover to what extent this approach meets their needs. This is done through a case study of the eight Scarborough Neighbourhood Improvement Areas (NIAs), which comprise an important share of diverse ethnic groups. This study reviews the NIAs participatory process and policies and uses interviews with representatives of the Scarborough communities, representatives of local neighbourhood agencies, and the City in order to examine participatory processes and uncover ethnic groups’ perspectives concerning the municipal participatory approach. Key Words: citizen participation, meaningful public engagement, citizen empowerment, ethnic communities, the City of Toronto participatory framework.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.026 | 0.015 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".