Evaluations of Rape: Investigations Using Implicit and Explicit Measures, Online Research Methodology, and Samples of Community Men
Bibliographic record
Abstract
Evaluations of rape theoretically play an important role in sexually aggressive behaviour (e.g., Nunes, Hermann, & Ratcliffe, 2013). The purpose of this dissertation was to explore the relationship between implicit and explicit evaluations of rape and sexually aggressive behaviour using a longitudinal research design online. Study 1 examined the use of a self-generated identification code (SGIC) to track participants anonymously online. Study 1A examined a six question SGIC in a sample of 168 students and found the SGICs produced unique identifiers, and a low, but acceptable, exact match rate across a one month period. Study 1B explored a 12 question SGIC with a sample of 30 students and 15 community participants. The 12 question SGIC also produced unique identifiers, and resulted in a low, but acceptable, exact match rate across a one week period. These results suggest the 12 question SGIC can be used in longitudinal research online.
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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 | 0.009 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".