Warmth, Competence, and Blame: Examining Mothers of Sexually Abused Children Within the Stereotype Content Model
Bibliographic record
Abstract
Research shows that nonoffending mothers are frequently held at fault for child sexual abuse (CSA), by both society and professionals, with contradictory explanations for the fault. For example, the same maternal characteristic can be used to assign blame or alleviate blame (i.e., single mothers have been held more at fault for their child's CSA and less at fault). The purpose of this study was to assess a theoretically based model that could account for these different reasons. We tested the stereotype content model (SCM), which examines the content of stereotypes toward target groups, by focusing on perceptions of that group's levels of warmth and competence. We sampled 136 undergraduate participants who read a vignette describing CSA, and completed the SCM with the mother of the victim as the target, and measures of mother fault. Our results showed that participants fell into three SCM groups of mother fault: (a) Moderate Contemptuous Prejudice (i.e., low competence, low warmth); (b) Admiration (i.e., moderate competence, high warmth); and (c) Very Contemptuous Prejudice (i.e., very low competence, very low warmth). Each cluster also held unique emotions toward the mother, as predicted by the SCM. Results further showed that assigned levels of fault were significant, but that fault did not vary by SCM group, lending support to the ideas that the SCM can be applied to this group and that different participants assign fault for different reasons.
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 | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".