Structural fire as a social problem: firehouse distribution disparities in New York City
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The victims of severe New York City blazes are most often immigrants, and the worst fires occur in neighborhoods with higher proportions of Black and Latinx residents. While race, socioeconomic status, and foreign-born characteristics have been established as fire risk factors, contemporary disparities in firehouse distribution at the neighborhood level remain an underexplored avenue of research. This study explores the following research question: To what extent are race, ethnicity, class, and foreign-born characteristics reflected in the distribution of firehouses within residential New York City census tracts? Using centroids in combination with census tract data in regression analyses, we find that ethno-racial composition is a significant factor, and that it is intertwined with class. Our analysis indicates that a higher percentage of Black and Asian residents is associated with greater distance to a firehouse and the relationship may be explained by class to a varying degree depending on neighborhood traits. Further, higher median household income is associated with closer proximity to a firehouse in predominantly white neighborhoods only. Overall, the analyses indicate how race relates to the distribution of firehouses in a global city where neighborhoods have been commodified.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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 it