Did They Start Out Homeless? Arrival Situations of Persons Experiencing Unsheltered Homelessness in Los Angeles County
Bibliographic record
Abstract
This study highlights how better demographic measurement more accurately informs public policymakers. In this era of renewed and more vigorous efforts to clear people experiencing unsheltered homelessness (unsheltered PEH) from city sidewalks, parks, and elsewhere, elected officials and other stakeholders need to clearly understand the dynamics of their unsheltered PEH populations. To advance understanding, this study investigated in-migration of unsheltered PEH in conjunction with the Los Angeles Continuum of Care’s 2024 HUD-mandated Point-in-Time count. In response to the measurement work of Mitchell (2024), the Los Angeles Homeless Services Authority’s 2024 Demographic Survey of unsheltered PEH instrument included a new item distinguishing between perceptions of a place where one has lived versus stayed, which revealed whether in-migration of individuals and families coming to the area immediately experienced homelessness or became unhoused after relocation. This analysis of over one-thousand survey participants self-identified as having migrated into Los Angeles County (a quarter of the total sample of unsheltered PEH) also explored whether there were associations with first-time homelessness or with being accompanied by household members. Responses to this complex survey were analyzed using Stata 17 MP. Key findings include that most survey participants reported either arriving homeless and unsheltered or first staying in either their own or a friend’s or family member’s home. More than half of all in-migration was initially to housed situations (more so for first-time homelessness), not into homelessness. Lone adults were more likely to experience homelessness immediately while accompanied adults were most likely to first stay in someone else’s home.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".