Diversity and social cohesion: the case of Jane-Finch, a highly diverse lower-income Toronto neighbourhood
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Diversity has increasingly emerged as the core focus of many studies concerning factors impacting on social cohesion. Various scholars have concluded that diversity is detrimental to cohesion. Most of this research, however, draws generalisations based upon quantitative data and fails to account for the impact of inequality, segregation and discrimination, and their interconnectedness to diversity. This research provides an in-depth qualitative analysis of the perceptions of inhabitants of a diverse Toronto neighbourhood regarding formal and informal interactions, common values and attachment. The findings suggest that the internalisation of gendered and class-based racism by inhabitants plays a crucial role in shaping perceptions and interactions.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.035 | 0.002 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it