“I Know Correlation Doesn’t Prove Causation, but . . .”: Are We Jumping to Unfounded Conclusions About the Causes of Sexual Offending?
Bibliographic record
Abstract
Identifying causes of sexual offending is the foundation of effective and efficient assessment, intervention, and policy aimed at reducing sexual offending. However, studies vary in methodological rigor and the inferences they support, and there are differences of opinion about the conclusions that can be drawn from ambiguous evidence. To explore how researchers in this area interpret the available empirical evidence, we asked authors of articles published in relevant specialized journals to identify (a) an important factor that may lead to sexual offending, (b) a study providing evidence of a relationship between that factor and sexual offending, and (c) the inferences supported by that study. Many participants seemed to endorse causal interpretations and conclusions that went beyond the methodological rigor of the study they identified. Our findings suggest that some researchers may not be adequately considering methodological issues when making inferences about the causes of sexual offending. Although it is difficult to conduct research in this area and all research designs can provide valuable information, sensitivity to the limits methodology places on inferences is important for the sake of accuracy and integrity, and to stimulate more informative research. We propose that increasing attention to methodology in the research community through better training and standards will advance scientific knowledge about the causes of sexual offending, and improve the effectiveness and efficiency of practice and policy.
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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 | 0.279 | 0.528 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.005 | 0.047 |
| Scholarly communication | 0.009 | 0.032 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".