Evaluative Attitudes Towards Sexual Offending Against Children: The Development of Self Report Measure Items
Bibliographic record
Abstract
The purpose of this study is to develop a measure of evaluative attitudes towards sexual offending against children.The secondary purpose is to explore the relevance of the items I created.I created 60 items that depict sexual offending towards children.Items were administered to inmates from the Missouri Sex Offender Program in Jefferson City, MO.Participants rated items using either one of two different Likert scales of 1-4.The items were refined according to highest variance, creating the Evaluative Attitudes Towards Sexual Offending Against Children scale (EASOC).Mean scores of each offence group were compared to see if those with a current hands-on sexual offences have less negative evaluative attitudes about sexual offending.The study will explore if there is a difference in EASOC responses between men with sexual offences against children and men with other offences, and possible connection between evaluative attitudes and sexual offending against children.i
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".