The link between composition of the neighbourhood and contact, and ethnic and racial resentment in Australia, Canada, Germany, Japan and the United States
Bibliographic record
Abstract
In this study, Stockemer examines the degree to which the composition of the neighbourhood and contact between natives and ethnically diverse minorities influence individual attitudes towards people of a different ethnic and racial background. By means of an original survey conducted in five diverse countries (United States, Canada, Germany, Australia and Japan) in October 2021, he finds that the ethnic composition of the neighbourhood, as well as the two proxies for contact (that is, the intensity of contact and the quality of contact) have explanatory power. Yet, contrary to the assumptions of the ethnic threat theory, it is predominantly native neighbourhoods, rather than mixed or ethnic neighbourhoods, that seem to trigger higher resentment towards people of a different ethnic and racial background, even if the relationship is only casual. When it comes to contact, Stockemer finds that it is the quality of contact (that is, positive versus negative), rather than the intensity of contact, that appears to serve as a bulwark against negative racial attitudes. These findings tend to be quite robust across the five cases.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| 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".