Co-ethnic concentration and trust in Canada’s urban neighbourhoods
Bibliographic record
Abstract
This study investigates the relationship between the density of people’s ethno-racial in-group in their neighbourhoods (co-ethnic concentration) and trust in their neighbours. Previous studies demonstrate that ethno-racial diversity decreases trust in others, however, these studies rely on overly broad definitions of diversity and of trust, and often do not disaggregate the effects for Whites and ethno-racial minorities. Hence, this study examines the relationship between co-ethnic concentration and trust, focusing on how this relationship may change depending upon one’s ethno-racial status. Putnam’s (2007) analysis leads to a paradox in the sense that, according to the same principle that predicts declining trust amongst Whites, increasing diversity should lead to greater levels of trust for ethno-racial minorities whose share of the population increases with diversification. The findings demonstrate that there is a positive relationship between co-ethnic concentration and trust in neighbours and that this relationship holds for Whites as well as ethno-racial minorities.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".