A Dynamic Approach to Risk Factors for Maternal Corporal Punishment in Early to Middle Childhood
Bibliographic record
Abstract
We investigated the utility of a dynamic approach to risk factors for early to middle childhood maternal corporal punishment. Archival data from Fragile Families and Child Wellbeing Study mothers (N = 4,364) were used to model the associations of several risk factors (maternal depression, maternal substance use, parental burden, child negative affect, and intimate partner violence victimization), repeatedly measured at child ages 1 and 3, with both the age 3 level of and age 3-9 linear change in corporal punishment. Unlike most previous research, we distinguished between two types of variation in risk factors, reflecting occasion specific fluctuations as well as their absolute levels. We concentrate herein on the potential role of occasion specific variation in the above risk factors at age 3 – variability that is not a carryover effect from a risk factor’s past level. Mothers who reported perturbations of (i.e., occasion specific variation in) depression, substance use, parenting stress, and child negative affect between ages 1 and 3 reported higher age 3 corporal punishment. Yet age 3 to 9 change in corporal punishment was not successfully predicted. The results underscore the importance of considering occasion specific fluctuations in risk factors, alongside their absolute levels, in the development and prevention of corporal punishment.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".