Participatory planning in a low-income neighbourhood in Ontario, Canada: building capacity and collaborative interactions for influence
Bibliographic record
Abstract
Abstract This research evaluated a community-led participatory planning process that sought to involve citizens who are often marginalized within planning processes. Participatory planning – which is theoretically informed by communicative planning theory – may shift the legacy of power and marginalization within planning processes and improve planning outcomes, foster social cohesion, and enhance the quality of urban life. The two-year Stewart Street Active Neighbourhoods Canada (ANC) project aimed to build capacity among residents of a low-income neighbourhood in Peterborough, Ontario and to influence City planning processes impacting the neighbourhood. The project, led by a community-based organization, GreenUP, fostered collaborative interactions between residents and planning experts and supported residents to build and leverage collective power within planning processes. The participatory planning approach applied in the Stewart Street ANC transformed – and at times unintentionally reproduced – inequitable power relations within the planning process. Importantly, we found that GreenUP was a vital power broker between marginalized residents and more formal power holders, and successfully supported residents to voice their collective visions within professionalized planning contexts.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.028 | 0.007 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| 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".