A social work study on parents’ income and personal characteristics and child abuse: A case study of city of Esfahan
Bibliographic record
Abstract
Child abuse is one of the most important issues in any society and any action to detect influencing factors could help take possible actions on its prevention.In this paper, we present an empirical study to find the impact of family income, occupation, size, age, education and drug addiction on growth of child abuse.The study uses a sample of 450 female students who were enrolled on guided schools in city of Esfahan, Iran.The study chooses 5 classes and in each school and 10 students are randomly selected.A questionnaire is designed and distributed among the sample people, which is categorized in four groups of physical, sexual, emotional and neglect child abuse.The results are analyzed using different tests including Pearson correlation test, Chi-Square, etc. to test different hypotheses.The results of our survey indicate that there are some meaningful relationships between different family characteristics including age, occupation, family size, educational background, and drug-addiction and child abuse.However, our survey does not provide any evidence to believe there is any relationship between home status and child abuse risk.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.009 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".