Ending homelessness in the Czech Republic: Application of the Housing First model to the Czech environment
Bibliographic record
Abstract
In times such as these, when there is a rapid increase of people facing a housing crisis in the Czech Republic and the attempts to create and enforce the Law on Social Housing, people look at models from the Western countries, such as Housing First. Such models could provide possible solution for the housing situation of many people and also improve the quality of their lives. This particular method appeared in the 1990s USA and was aimed especially for people without homes who suffer from a mental disorder and alcohol or drug addiction at the same time. The main idea is that housing should not be a reward for successful solution of life problems but something that a person needs to start dealing with their troubles. In cases when people are given a place to live for a reduced rent, with the help of a social worker they can very often keep it up and they have the chance to deal with other problems connected to homelessness. The efficiency of the model is explored through experimental projects that do not look only at the percentage of people who are able to keep up their home but also the positive impact on their health or employability. With the spread of this method to Canada, Europe and Australia, certain aspects of Housing First were adapted to local contexts. Many subsequent projects use only...
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".