Can cognitive maps provide insight into women who hurt their children?
Bibliographic record
Abstract
Efforts to organize information relative to complex, multifaceted issues such as fatal and nonfatal child abuse can be daunting but are essential to the development of adequate strategies for recognition of at-risk mothers and, ultimately, for prevention-focused interventions. The purpose of this exploratory descriptive pilot study using quantitative and qualitative techniques was to determine what constellation of antecedents broadly categorized as vulnerabilities and triggers may be associated with fatal or nonfatal abuse from the viewpoint of the mother/perpetrator. Due to challenges in accessing the study population, a convenience sample of 88 women incarcerated for fatal and nonfatal abuse was drawn from four women’s prisons in three Southwestern United States. A structured questionnaire consisting of 68 paired comparisons was administered to participants. Participants rated the vulnerabilities and triggers for dissimilarity and ranked their relative importance. Priority ratings were obtained from responses to content analysis questions. Multidimensional scaling (MDS) and clustering methodologies were used to generate cognitive maps of the concepts in relation to one another. The two groups shared common underlying structures consisting of three disparate clusters, “Family Intimacy”, “Negative Emotion”, and “Abuse”. They diverged in vulnerabilities and triggers that had the most profound impact on their thinking and behavior. Both groups had a similar underlying structure for antecedents to these crimes, but evaluative responses showed differences between women convicted of fatal versus nonfatal abuse.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".