EVALUATION OF THE EFFECTIVENESS OF FACE-TO-FACE AND ONLINE MENTORING USING ZOOM MEETING IN CHANGING DEVIANT BEHAVIOUR IN JUNIOR HIGH SCHOOL STUDENTS
Bibliographic record
Abstract
The world of school adolescents is an interesting dynamic. Generally, they want new things that they have never tried before. Something new if it implies positive actions is certainly not a problem, but if it leads to negative actions this will cause problems. School adolescents who have problems and can solve their own problems are positive. However, on the contrary, if they have a problem and cannot solve it themselves and vent it to negative actions, this means that they need the help of others. Simply put, in the perspective of guidance and counselling, people who help solve other people's problems are called counsellors. This counsellor is expected to help adolescents with problems to find the best solution according to the lightness and severity of the problems faced by these adolescents. The efforts to prevent adolescent deviant behaviour are creating a harmonious family, not generalising between adolescents with one another, developing adolescents through education, encouraging adolescents to be active in organisations, developing adolescents through interests and talents supported by routine coaching by counseling teachers both in writing, face-to-face and online using Zoom Meeting.. It is hoped that even though juvenile delinquency can be reduced so as to produce good academic and non-academic learning achievements. And then the handling techniques for adolescent deviant behaviour are: first, individual handling which includes giving instructions or advice, counselling, and psychotherapy, second, family handling, third, group handling and fourth handling of couples
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".