Qualitatively Understanding Mother Fault After Childhood Sexual Abuse
Bibliographic record
Abstract
Socially constructed images of motherhood suggest that a "good" mother is caring, nurturing, and selfless-the perfect maternal figure. When these standards are not met, mother blaming (i.e., assigning fault to mothers) occurs even in child sexual abuse (CSA) cases. We collected 312 open-ended responses in total from 108 community-based participants to understand contextual factors that increase and decrease in mother fault in a CSA-related vignette depicting the mother's partner as the perpetrator. Thematic analysis revealed five main themes. Three themes were associated with decreased blame: Lack of Overt Knowledge (i.e., the mother had no direct knowledge of the CSA and thus cannot be blamed), Physical Act (i.e., the mother was not the actual perpetrator; only the perpetrator is responsible for the CSA), and Trust (i.e., the mother should be able to trust her partner). Two themes were associated with increased blame: Covert Knowledge (i.e., the mother was expected to have covert, intuitive knowledge of the CSA) and Mistrust (i.e., the mother should have known better than to trust her partner). Faulting mothers for the CSA of their child may reduce reporting of, and help seeking for, CSA, due to fear of being blamed.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| 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".