A drug addict, a model and an engineer walk into a bar: Victim dehumanization and violations of sexual consent.
Bibliographic record
Abstract
The amount of humanness that we ascribe to others is influenced by a variety of factors, including demographic variables and other personal level information such as their social class, age and cognitive capabilities. Humanization has been shown to influence the severity of our reactions to violence directed at others and influence our judgements of the violator’s thoughts, motives and actions as well as victim responsibility and deservingness to be protected (Bastian et al. 2011). Within the context of sexual encounters, we also form impressions of the people involved, where the humanness ascribed to each person could affect perceptions of the encounters. Participants read two stories describing non-consensual sexual encounters. Two male characters were created, one for each story. Eight female character profiles encompassing a range of victim profiles that varied in age, occupation, social classes and other demographics were created. These profiles were also manipulated in a particular way to encourage a more or less humanized impression. Female profiles were crossed with each story, and participants saw both males and two of the females. Participants then responded to a set of open- and close-ended questions which assessed the agreement with a set of statements to determine their perceived level of consensuality of the interaction, moral outrage by the male’s violation, severity of legal wrongness, and severity of punishment deserved for the actions read about. This presentation will discuss how victim variables and our construal of them, including age, occupation, perceived coldness and intelligence, influence reactions to non-consensual sexual encounters. Faculty Mentor: Aimee Skye Department: Psychology (Honours)
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| 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".