Collective Capabilities Complete a Neighbourhood
Bibliographic record
Abstract
This article argues for the need for attention to agency as well as structure in planning for complete neighbourhoods and communities, drawing upon collective capabilities theory and driven by a community-engaged research approach. While complete communities planning proposes to provide more fulsome social and physical infrastructure to residents in a context of urban growth and change in Canadian cities, contemporary efforts tend to neglect or disdain the agency and empowerment of residents. This logic and rationale for complete communities planning has shifted compared to the origins of neighbourhood planning in Canada, as will be exemplified here drawing upon the case of Vancouver. The application of the theory of collective capabilities in complete communities planning offers a path forward that is not naïve to the challenges posed by participatory planning and that views organizations other than the government as having collective capabilities to plan. We demonstrate the potential of this through the case of our community-engaged research partnership based at the South Vancouver Neighbourhood House. The project mobilized spatial and statistical research to document the extent of inequities and needs experienced in South Vancouver neighbourhoods as well as the collective capabilities of residents working through the neighbourhood house hub to provide essential services and do effective neighbourhood planning.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.013 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".