First steps in the development of a new measure of attitudes toward sexual offending against children
Bibliographic record
Abstract
It is unclear whether existing measures of attitudes and cognitive distortions regarding sexual offending against children (SOC) reflect evaluative attitudes toward SOC (i.e., how negatively or positively one views SOC). The purpose of the current study was to take the first steps toward creating a self-report measure of evaluative attitudes toward SOC. We created 30 items and asked 157 incarcerated people in a sexual offense treatment program to complete them. We retained the 13 items that were the least positively skewed (i.e., lowest endorsement of the most negative response option) and non-redundant (i.e., not too highly correlated with other items) for inclusion in the new measure, which we called the Evaluative Attitudes Toward Sexual Offending Against Children (EASOC) Scale. As an initial test of the relevance of the EASOC Scale, we examined its association with SOC. Participants with SOC (n = 58) reported more positive evaluative attitudes on the EASOC Scale than did those without SOC (n = 22). This expected association is a necessary (but not sufficient) indication that the EASOC Scale may be relevant for predicting and explaining SOC. Future research using more rigorous methodology should build on our modest first steps to revisit item selection and test the validity and relevance of the EASOC Scale.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.002 |
| 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".