The Effect of Economic Standing, Individual Preferences, and Co-ethnic Resources on Immigrant Residential Clustering <sup/>
Bibliographic record
Abstract
Our study examines how immigrants cluster in co-ethnic neighborhoods. We systematically explore the effects of three factors on the co-ethnic clustering of immigrants: economic resources, co-ethnic preferences, and the use of co-ethnic information sources. The study is based on a unique data set that provides rarely available rich information on housing search collected in Toronto in 2006. Focusing on Asian Indians and Chinese immigrants, the results clearly suggest that of all preferences, only co-ethnic preference is related to co-ethnic clustering of the two groups when income and use of co-ethnic resources are taken into consideration, and that levels of co-ethnic clustering are not related to the economic resources of immigrants. The findings also reveal that some effects are distinctive to specific groups. Although immigrants use various co-ethnic resources to obtain housing information, only the use of co-ethnic real estate agents is significant, and that only for the clustering of Chinese, not for Asian Indians.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".