“STUDENTS FOR CHILDREN”: A VOLUNTEER PROGRAMME-MODEL FOR UNIVERSITIES FOR THE SUPPORT OF CHILDREN IN FOSTER CARE
Bibliographic record
Abstract
Foster care institutions are badly understaffed and operate on the lowest expected standards in terms of human resources in Hungary. In many cases, child protection personnel working with children in foster care do not have the necessary qualifications, and even those that do are often so overloaded with tasks that they cannot routinely engage in meaningful social interactions with the children. This paper introduces a unique and easily adaptable model of volunteer ,,work in university settings that aims to improve the situation of children in foster care. The Students for Children Volunteer Programme was founded in 2010 in the Faculty of Law at the University of Pécs, Hungary, and is now part of the curriculum there both as an elective course and as a cross-faculty programme. From the outset, the primary goal of this initiative has been to improve the situation of children in foster care through student mentoring by empowering them to manage everyday challenges and develop meaningful perspectives on their futures. Other equally important objectives are to enhance students’ social sensitivity and skills and to shape their thinking through this challenging work. Since its inception, the programme has been operating with unbroken success and, over the years, nearly 400 volunteers have completed the programme. The long-term plan is that through this model a country-wide network of similar volunteer programmes can be developed to improve the situation of children in need. Although aspects of the Students for Children programme still need to be refined, our experience with it shows that it has invaluable social, educational, and psychological effects on both the children and the future law professionals.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".