Nurturing Inclusive Urban Futures: Valuing the Contributions of Community Organizations in Ontario Cities
Bibliographic record
Abstract
This dissertation investigates the experiences of community organizations in three Ontario cities— Cornwall, Kingston, and Ottawa—as they grapple with building municipal relationships and navigating community development processes in their cities. Community organizations, informal collectives formed around shared identity or goals, play a vital role in contributing to the liveliness and wellbeing of urban communities. Regardless of their varied socio-spatial contexts and mandates, which include community support, activism, inclusion, arts, and heritage activities, these organizations often extend their efforts to fulfill essential care work for their communities and address service deficiencies for underserved populations. Yet, they are frequently underrecognized and unsupported by municipalities, who do not understand or value their contributions. Community organizations encounter significant logistical, spatial, financial, and operational challenges that place their communities and those they support in positions of precarity. These issues stem from structural oppression embedded within neoliberal paradigms and urban power systems. These systems of oppression limit municipalities’ purported attempts to engage in equitable, diverse, and inclusive community development practices. The findings of this dissertation are grounded in a range of voices, centering the geographical imaginations and experiences of participants through various research communication strategies. In line with the principles of critical praxis-oriented research, this study engages in responsive knowledge mobilization through the creation of alternative research communication formats including zine work and report creation, being attentive to the audiences that research might benefit. This approach ensures that the research not only contributes to academic discourse but has practical implications for the communities it studies. It considers the potential value of these findings for municipalities striving to create more inclusive communities and brings forward the importance of recognizing and addressing the challenges faced by community organizations in their efforts to engage in care work and create a sense of place and belonging in their 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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.045 | 0.026 |
| Scholarly communication | 0.013 | 0.004 |
| Open science | 0.002 | 0.013 |
| 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".