An Analysis of Reasons for Placing Children in Establishments Intended for the Execution of Constitutional and Protective Care
Bibliographic record
Abstract
My Diploma work is called “Reasons for Children´s Placement in Institutional and Correctional Facilities“. The aim of the work was to find out and analyse why children were transferred into the facilities of institutional and correctional education and to map all legal measures which could procede. The theoretical part deals with socio-pathological phenomena in families, legal framework of placing children in special-care facilities, children´s social and legal security, different school institutions and their inmates´ rights and duties as well as psychological impact of institutional care on children´s development. In the practical part a method of quantitative sociological research, a secondary data analysis, has been used. The research database consisted of all children who were placed in any special-care facility as a result of court-ordered institutional and correctional education, preliminary measures or parent agreed-to placement in an diagnostic institute. Three hypotheses based on the professional literature findings were defined. 1. The main reason why children are placed in institutional facilities is a dysfunctional family where the children´s education is threatened by some sociopathological phenomena. 2. Children are mostly placed in the institutional facilities aged 12 - 15. 3. The fact schools do not solve the behavioural problems in children in the long term affects negatively more than one quarter of institutionalised children. The first hypothesis was not confirmed while the other two were.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".