Bibliographic record
Abstract
The aim of the study was to develop a reliable self report measure to identify maltreatment among children—across a broad range of parental abusive and neglecting behaviours towards children— that could be used to evaluate different levels and types of abuse and neglect, that is, physical abuse, emotional abuse, physical neglect, emotional neglect and sexual abuse among children of the age ranging from 8–12 years with specific reference to Pakistani cultural context. This indigenous measure comprised 34 items indicative of parental abusive and neglecting behaviours towards children, and was further organised into four categories of abuse and neglect: physical abuse, emotional abuse, physical neglect and emotional care/neglect (low score on which was indicative of emotional neglect). A sample of 200 children (100 boys and 100 girls) taken from six different cities of Punjab indicated the measure to have a highly significant internal consistency measured by coefficient alpha for the total scale and its four sub-scales. Construct validity was determined with the help of Principal Component Factor Analysis that resulted in four-factor solution. Finally, the self report measure termed as Child Abuse Scale (CAS) comprised 34 items pertaining to four major categories of abuse and neglect, and these were labelled accordingly. The reasons for the exclusion of fifth category of sexual abuse have been discussed with special reference to Pakistani cultural and social set up.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".