Working Toward Spatial Justice Through Planning
Bibliographic record
Abstract
This paper explores the development of Community Benefit Agreements (CBAs) and Community Benefit Frameworks (CBFs) in Toronto through the examination of four local case studies⎯Rexdale-Casino Woodbine Community Benefit Agreement; Jane⎯Finch Metrolinx Community Hub; Parkdale Community Benefits Framework; and the City of Toronto Community Benefits Framework. CBAs are legally binding agreements between developers and community groups which aim to ensure that local communities can receive benefits from urban development projects. The study sheds light on the role of CBAs in promoting social justice and community empowerment, highlighting their potential significance in marginalized neighborhoods by providing decent work, affordable housing, and spaces for community use. This paper views these case studies through a spatial justice perspective which critically examines the formation, regulation, and the use of urban space with an emphasis on the use-value of space by acknowledging the attachments individuals have to their neighbourhoods. This paper argues that through CBAs⎯which seek greater community inclusion in the planning and development process⎯the alienation and displacement of community members can be resisted against. This study contributes to the emerging discussion of CBAs as a planning tool, in addition to the broader scholarly and planning discussions on urban development and social equity, offering insights for policymakers, community advocates, and urban planners. In addition, this paper makes the case for continued expansion of resources dedicated to CBAs as they can serve as a planning tool which can aid in fostering community resilience, reducing the displacement and alienation of people within their own communities, and promoting more equitable development within cities.
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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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.021 | 0.078 |
| Scholarly communication | 0.016 | 0.011 |
| Open science | 0.003 | 0.024 |
| Research integrity | 0.003 | 0.005 |
| 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".