Fostering a Sense of Belonging in Toronto – A Case Study of Dixon
Bibliographic record
Abstract
Using the neighbourhood of Dixon as a case study, this project examines what factors influence Dixon residents’ sense of belonging to their neighbourhood. Interviews conducted with twelve residents outline how characteristics such as a lack of up-keeping of the neighbourhood’s physical state, a negative public perception of the community formed by media outlets, and hyper-policing initiatives within the neighbourhood all contribute towards a negative impact on residents’ quality of life by influencing their relationship with their space, their community, and themselves. The paper expands on this notion by examining the deep-rooted history of othering experienced by Dixon residents, dating back to the 1990’s when Somali refugee claimants initially settled into Dixon, earning it the nickname Little Mogadishu. This study is concerned with the lived experiences of Dixon residents and how various forms of political, social, and economic othering of their community has shaped their perception of place-belongingness, as well as their distrust in any body of power that can challenge their ability to foster a sense of belonging to their neighbourhood.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.031 | 0.009 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 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".