The Implicit Relational Assessment Procedure as a Measure of Sexual Orientation in Heterosexual, Bisexual, and Lesbian/Gay Men and Women
Bibliographic record
Abstract
Previous research has suggested that scores generated using a sexual orientation Implicit Relational Assessment Procedure (IRAP) correlate with self-reported sexual orientation in gay and heterosexual men and have a strong ability to differentiate the two groups (Timmins et al., 2016). The present study sought to replicate this work and to determine how a range of groups that differ in terms of self-reported sexual orientation perform on the measure. Lesbian/gay, heterosexual, and bisexual women (n = 99) and men (n = 96) completed a sexual orientation IRAP and self-report measures of sexual attraction and behavior. The IRAP involved responding "True" or "False" to pictures of nude males and females paired with words meaning attractive or unattractive. Participants were required to respond as if all men were attractive and all women were unattractive for half of the IRAP's test blocks, and vice versa for the other half. Response latencies were recorded, and D-IRAP scores were calculated for overall responses, female responses, and male responses. Significant correlations were found between all D-IRAP scores and all corresponding self-report measures for men (rs = .38-.64). This was also true for women (rs = .24-.53) with a single exception: the D-IRAP score for responses to men with self-reported sexual attraction to men. Similarly, almost all D-IRAP scores significantly differentiated sexual orientation groups (areas under the curve = .64-.92), apart from bisexual and lesbian/gay women. These findings suggest the IRAP is a useful ex situ measure of sexual orientation.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".