A Comparison of Canadian and American Offender Stereotypes
Bibliographic record
Abstract
When asked to think about the characteristics of criminal offenders, what comes to mind? A stereotype is an inflated belief associated with a particular used to justify (rationalize) our conduct in relation to that category (Allport, 1979, p. 191). Research on offender stereotypes has suggested that people do hold stereotypes of offenders (MacLin & MacLin, 2004) and that these stereotypes can affect one's perceptions of defendants (Yarmey, 1993). Thus, it is important to examine stereotypes because such beliefs can affect legal decision-making (Landy & Aronson, 1969). Some researchers have studied which demographic characteristics are associated with the general offender stereotype. For example, participants in Reed and Reed (1973) perceived the typical criminal as an uneducated male who had psychological issues. Madriz (1997) found that the typical criminal was male and Black and/or Hispanic. Some of her participants also described criminals as immigrants. MacLin and Herrera (2006) asked participants to list the first ten things that came to mind when they heard the word criminal. Here, the typical criminal was seen as male. In terms of race, Blacks had the highest ranking (40%), followed by Hispanic (30%), White (20%), and Asian (10%). MacLin and Herrera's (2006) study suggested that the typical criminal was seen as a male, a visible minority, and on average, 25 years old. When it comes to social categories, race has been a major focus in stereotype research. In one study, participants were asked to rank different and the likelihood that they are perpetrated by various racial groups (Gordon, Michels, & Nelson, 1996). The results showed that Blacks were seen as more likely than other racial groups to commit blue-collar crimes, such as aggravated assault, motor vehicle theft, and violent offenses. Whites, in contrast, were seen as more likely to commit white collar crimes such as embezzlement, forgery, and fraud. Similarly, Welch (2007) noted that in the United States, Blacks/African Americans are perceived as more likely to be offenders in general, perpetrators of violent in particular, and that these stereotypes contribute to racial profiling. There is some suggestion that such stereotypes of Blacks hold across cultures. Henry, Hastings, and Freer (1996) surveyed Canadian community members. They found that 37% of participants believed that there is a relationship between racial/ethnic group and the likelihood that a person would be involved in crime. Of the participants who linked race and crime, a majority (61%) believed that the groups most responsible for crime were Jamaicans, other West Indians, and Blacks. Stereotypes regarding gender and crime typically have focused on views of women as victims of crime (Howard, 1984). In terms of female offenders, there have been some studies on women as perpetrators of rape. Specifically, several studies have noted that females are not seen as typical perpetrators of rape, especially the rape of male victims (Smith, Pine, & Hawley, 1988; Struckman-Johnson & Struckman-Johnson, 1992). As a result of such gender stereotypes, female offenders are often treated more leniently than male offenders in cases involving rape (Davies, Pollard, & Archer, 2006; Smith et al., 1988) and robbery (Ahola, 2012). Researchers have focused less on the stereotype concerning the typical offender's age. In the psychology and law realm, what research exists on age and stereotypes has tended to focus on older adults and children as victims and witnesses rather than as offenders (Lachs et al., 2004; Mueller-Johnson & Ceci, 2007; Ross, Dunning, Toglia, & Ceci, 1990). One age group that has received some recent attention in the stereotype literature is that of juvenile offenders. Haergerich, Salerno, and Bottoms (2012) suggested that there may be two subcategories of the juvenile offender stereotype: Superpredator and Wayward Youth. …
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".