A Hobo, a Bimbo, and an Orphan Walk into a Bar: An Examination of Victim Dehumanization and Reactions to Consent Violations
Bibliographic record
Abstract
Dehumanization involves failing to acknowledge that another person has the full suite of emotions, cognitions and capabilities associated with being human. Recent work has described two distinct types or dimensions: mechanistic and animalistic (Haslam, 2006; Haslam & Loughnan, 2014). Mechanistic dehumanization involves denying someone characteristics of human nature such as emotionality and interpersonal warmth, and a tendency to perceive them as cold, rigid and lacking in animation, much like automata. Animalistic dehumanization involves perceiving someone as devoid of uniquely human characteristics such as civility and higher cognition, and a tendency to view them as coarse, unintelligent and immoral, much like animals. Dehumanization on either dimension has consequences for our ability to see others as victims, and to experience moral outrage at their victimization (Gray, Gray & Wegner, 2007; Bastian et al., 2011). Our project will examine whether dehumanization of female victims on either dimension influences our assessment of the wrongness of male consent violations and deserved punishment. Participants will read scenarios of heterosexual encounters containing consent violations by a male. The female will be varied between subjects to reflect a profile that encourages humanization or dehumanization on one or both dimensions. Participants’ moral outrage at the male as well as the severity of punishment deserved will be assessed. We expect dehumanization along either dimension will attenuate both measures. However, mechanistically dehumanized females are regarded as cognitively capable, and thus more blame-worthy and less deserving of protection. Those victim scenarios may experience more attenuated levels of outrage and punishment. Discipline: Psychology (Honours) Faculty Mentor: Dr. Aimee Skye
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".