Ethnic Discrimination in the Rental Housing Market: An Experiment in New Caledonia
Bibliographic record
Abstract
This study focuses on the links between ethnic discrimination, housing discrimination, and the ethnic composition of neighborhoods at a specific spatial level, that of the city quarter. Our goal is to determine whether discrimination exacerbates residential segregation. We measure discrimination and access to housing in Greater Nouméa, the capital of New Caledonia, by ethnic background, distinguishing between the people of Kanak (the indigenous people) and those of European descent. Between October 2015 and February 2016, four applicants individually responded to 342 real-estate rental ads, made a total of 1,368 responses. Two of the applicants made their Kanak origin known through their surnames, while two others similarly made their European origin known. In each pairing, an applicant signaled financial and professional stability by explicitly indicating that he was a civil servant. A particularity of the study was to analyze these data statistically by crossing it with the ethnic distribution of neighborhoods. Severe discrimination regarding access to private rental housing for Kanak applicants in all neighborhoods was found. Signaling stability strongly reduced discrimination against Kanak applicants. This discrimination is linked to the behavior of landlords and, to a lesser extent, to the actions of real-estate agencies. The difficulties accessing housing are solely due to discrimination linked to the social precariousness of Kanaks in neighborhoods where Kanaks are most represented. They are also linked to ethnic discrimination against Kanaks in neighborhoods dominated by Europeans. Housing providers thus play an active role in residential segregation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".