Housing first & homelessness challenges: A focus group with international experts
Bibliographic record
Abstract
Homelessness is becoming an expressed concern on the political realm expressed by the different political sectors, governmental bodies in the European Union and across other territories. Critical events related with Homelessness (e.g. deaths, violence both as victims and perpetrators, children being born on the streets) with variations, receive some attention from some media. However, we are experiencing a somewhat contradictory situation, on one hand, governments express concern with the phenomenon, support the emergence and the consolidation of national strategies, allocate programmatic resources (budget) to provide relief responses and the general public is concerned with those affected by the fact that some people despite the age, gender, ethnical background, health or mental health status remain for variant periods on the streets of the cities with a larger incidence on larger metropolis; on the other hand effective change in societal response is low and the situation essentially prevails. Within the HOME_EU: Reversing Homelessness in Europe Project (H2020 GA/ 726997), we were able to gather experts from the United States Marybeth Shinn (University of Vanderbilt), Sam Tsemberis (Pathways to Housing), Ken Matton (University of Illinois at UrbanaChampaign), Paul Toro (Wayne State University), and Europe, Ronni Greenwood (University of Limmerick), and José Ornelas, ISPA – Instituto Universitário) in Padova (June, 2018) around the core questions: a) why is Homelessness is a prevailing condition; and b) Why are we not being able to solve the problem? (We know about solutions, and effective responses.) The debate pointed that the major challenges include the variations of interest and the lack of consistent and continued efforts to address homelessness as a political priority; the shortage of Housing policies on availability and affordability; as a positive note the renovated national strategies may represent new in some cohorts opportunities, mostly advanced and pressed by civic and grass-roots movements, and some homeless studies (e.g. At Home/ Chez Soi in Canada, Chez Soi d’Abord in France, and now the HOME_EU with eight European Countries) contribute to the advancement of Housing First as a privileged model to transform Homelessness into an exceptional social emergency
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.003 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".