Residence registration to cope with homelessness: evidence from a qualitative research study in Milan
Bibliographic record
Abstract
Abstract Homeless people are one of the most vulnerable and marginalized groups in developed countries, and their homelessness situation often persists over the long term. However, so far, no studies have explained the specific role played by residence registration as it relates to deprivation amongst the homeless population and its contribution to improving the lives of homeless people. This paper investigates the paths homeless people in Milan use to access residence registration, via a case study in the city of Milan. Home to Italy’s largest homeless population, the city of Milan has implemented the innovative ResidenzaMi project to improve access to residence registration for homeless people. The study considers official statistics and individual interviews with service providers involved in the registration process. It further investigates the main factors impeding the registration process and outlines the consequences of the COVID-19 pandemic. Results from our study indicate that a residence certificate plays a critical role in helping homeless people exercise their rights and access the services they need to escape homelessness. Our findings suggest the importance of a holistic, multidimensional approach to ensure access to residence registration for homeless persons.
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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 | 0.012 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".