Why Does Incest Occur? A Distinction Between Genetic and Sociolegal Incest
Bibliographic record
Abstract
The present study compares and contrasts intrafamilial biological parent, intrafamilial step parent, and extrafamilial sex offenders who are ages 19 and older and have sexually offended against victims ages 12 and under, on several variables of interest. Sexual assaults reported to the Edmonton Police Service from 2010 to 2014 were included in the sample. The aim of this study was to explore whether there were distinct differences among the three groups. Based on past literature, we hypothesized the following: (a) Intrafamilial offenders will likely be older than their extrafamilial counterparts; (b) There is likely to be no difference in perpetrators’ sex; (c) Any difference in perpetrator’s ethnicity is uncertain; (d) The likelihood to reoffend will likely be greater for extrafamilial offenders; (e) Intrafamilial victims will likely be younger than extrafamilial victims; (f) There is likely to be no difference in victims’ sex; (g) Intrafamilial sexual abuse will likely last longer than extrafamilial sexual abuse; and (h) There will be greater delay in time to report abuse by intrafamilial offenders than extrafamilial offenders. We further expected to find that intrafamilial step parent sex offenders would reoffend more than biological parent sex offenders. The findings will be presented in this poster presentation. Faculty Mentor: Sandy Jung Department: Psychology
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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 | 0.001 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".