The Effects of Racial Identity, Racial Similarity and Typicality of Sexual Encounters on Perceptions of Consent
Bibliographic record
Abstract
To our knowledge, there has been little or no empirical investigation of whether racial identity and racial similarity of participants influences how consensual an encounter appears to be. This project examines that question, for sexual encounters that script-typical and -atypical.Bias against interracial couples has been empirically documented in studies suggesting that a reaction resembling disgust is experienced towards images of interracial couples (Skinner & Hudac, 2017). Not only has this bias been documented implicitly but also explicitly through respondents' reports. (Skinner & Rae, 2019). We know that emotions such as disgust can be used to inform decision making, moral judgments and perceptions (Haidt, 2001; Damasio, 1994). In this way, feelings of disgust toward interracial couples could affect judgments about the consensual nature of their sexual encounters. In addition, we examined whether racial identity and similarity influence our consent judgments more when the sexual encounter involves atypical rather than typical acts. Participants were presented with a typical or an atypical sexual encounter in which consent was equivalently portrayed. Before the story, participants were shown pictures of the male and female characters in the encounter. Racial identity (white/black) of both characters was randomized across participants. After reading the encounter, participants responded to items eliciting their perceptions of the consensual nature of the encounter as well as their feelings of disgust towards the characters. We reasoned that the negative affect one experiences toward inter- or other-race couples, here in the form of disgust, might inform participants' judgments about how consensual a sexual encounter is, perhaps especially when the encounter is not typical. Results demonstrated that male or female race as well as typicality of the encounter played no significant role on the perceived consensuality of the encounters. Participants did report higher ratings of disgust in the atypical encounters then the typical encounters. It was also found that male race did have an effect on disgust rating for male and female characters. Faculty Mentor: Aimee Skye Department: Psychology NOTE: This work is available as an abstract only.
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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 | 0.003 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".