Prevention and Correction of Juvenile Neglect
Bibliographic record
Abstract
The problem of juvenile neglect in recent years has gained particular relevance since the phenomena of family’s non-participation in the life of the child as well as children in their own lives have spread. The article reveals the essence, the main causes of neglect, or homelessness. Juvenile neglect is a reflection of the negative processes caused by economic factors, low spiritual and moral values of the population, the growing number of delinquency among adolescents and young adults, the problem of alcoholism and drug abuse, an insufficient number of methodological literature on this issue. The article describes the characteristic features of neglected teenagers: intellectual rigidity, proneness to conflict and the inability to communicate with people, alienation, irresponsibility and indifference to the fate of others, self-doubt. It also considers a system of corrective and preventive measures to stop juvenile neglect. The experimental work involved 132 school teenagers from Kazan (Republic of Tatarstan). For the implementation of the experiment there were used the following techniques: “The High School Personality Questionnaire (HSPQ) of Cattell, methods of “Diagnosis of the social and psychological adaptation” by Rogers and Diamond, Parental Attitude Questionnaire (PAQ) of Varga and Stolin, as well as methods of mathematical statistics, Student’s t-test to check hypotheses for the reliability of mean difference.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".