“Everybody out there in the real world is one paycheck away from being homeless”: job loss and housing precarity among people experiencing homelessness
Bibliographic record
Abstract
The United States has fewer labor protections and wage guarantees than most wealthy industrialized nations; thus, workers can fall into economic hardship by the loss of employment, reduction in work hours, or work-related accidents or injuries. Even when fully employed, low wage workers typically face substantial rent burdens, which place them at risk for being evicted from their homes and ultimately falling into homelessness. This paper examines the role of unemployment and underemployment in increasing the risk of homelessness and the ways in which job loss precipitated homelessness. The results are based on the qualitative findings of a large mixed method representative study of homelessness in California (USA). Occupational settings impacted workers' vulnerability to job loss and subsequent homelessness, particularly in the context of illness and injury, the societal impact of the COVID pandemic, and the role of probation and parole as barriers to steady employment. The resulting pathways from job loss to homelessness could be either sudden or gradual. Using data from in-depth interviews, we describe the characteristics of occupational settings that left workers vulnerable to unemployment-related job loss and subsequent homelessness and offer policy suggestions for addressing these issues.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".