The Many Misspellings of Albuquerque: A Comment on 'Sorting or Steering: The Effects of Housing Discrimination on Neighborhood Choice'
Bibliographic record
Abstract
This comment revisits the analysis in Christensen and Timmins (2022). We identify two critical errors used in the original analysis, one with the data and the other with coding. When either error is corrected several major results in the paper change, either in statistical significance or in effect size. The data error is a result of including fixed effects for the string variable 'city'. The raw variable is case sensitive and has many spelling mistakes. The coding error involves assigning a value of zero for the variable "of color" to both individuals identified as 'white' and as 'other' in the raw data. The level of clustering in the paper is also arguably too fine. Many of the results are not robust to clustering at the city level, as opposed to the subject pair level. In total, we affirm the authors' overarching claim of substantial and nuanced housing discrimination against racial minorities generally, and African Americans in particular; however, the effect sizes and significance are generally (although not always) smaller than the original authors findings. Additionally, there are several instances where the effects of discrimination on African Americans are no longer statistically significant but the effect of discrimination on Hispanics becomes significant.
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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 | 0.013 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.009 | 0.008 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.009 | 0.004 |
| Research integrity | 0.036 | 0.031 |
| Insufficient payload (model declined to judge) | 0.012 | 0.007 |
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".