How Summary Eviction Proceedings Fail Individuals Facing Housing Discrimination
Bibliographic record
Abstract
Every year, over three million American households are threatened with eviction from their homes. The consequences of eviction are “dire” and affect “every facet of life” that go beyond someone’s physical safety and livelihood. For instance, evictions may leave people unhoused, “[fracture] the integrity of their families, [crush] their livelihoods, [damage] their mental and physical health and their safety, [deprive] them of their place in community and, ultimately, [tear] apart the fabric of their communities.” While Americans of all backgrounds face evictions, there are often large racial, ethnic, and gender disparities among those who face eviction with Black Americans, women, and those with children being the most vulnerable. Specifically, “[n]early a quarter of Black tenants live in a county where the eviction rate for Black tenants is double the rate for white tenants.” Additionally, Black women with children were threatened with eviction at a rate of 28% while those without children faced a rate of 16%.\nThis post was originally published on the Cardozo Journal of Equal Rights and Social Justice website on February 2, 2024. The original post can be accessed via the Archived Link button above.
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 | 0.015 | 0.088 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.028 | 0.006 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.004 | 0.011 |
| Research integrity | 0.015 | 0.016 |
| Insufficient payload (model declined to judge) | 0.054 | 0.009 |
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".