Differentiated-Motivation Program as a Re-Socialization Tool of Homeless People
Bibliographic record
Abstract
Transformation of social sphere in Slovakia after 1989 caused many changes resulting in extensive reforms in social and economic fields. A new phenomenon of social exclusion and poverty started to occur. Homelessness became extreme form of exclusion. At that time, homelessness was not at the forefront of interest of professionals, as far as there were only rare cases of homeless people. However, continuous economic changes caused significant increase of this phenomenon and suddenly it was necessary to investigate it and find out solutions for elimination of this phenomenon. This paper deals with the possibilities of intervention of homeless people staying in the shelter. We verified potential of selected active measures within the application of differentiated-motivation program both methodologically and by our research. Our findings record fruitfulness of intervention form in the process of re-socialization within a context of finding a job. Our intention was to contribute to searching of more efficient social help for homeless people. We met with positive difference between the differentiated-motivation program and currently offered motivation strategies. On the basis of the results, we recommend to perform interventions verified by research in the work with homeless people. Social work with homeless people is very important, because social workers should be able to ensure new interventions and programs. Regarding the research file and set objective, we used a method of experiment in our research and collected data were evaluated and statistically processed in SPSS program.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".