Mainstream Superhero Comics and Gendered Representations of Social Control
Bibliographic record
Abstract
In the context of increasing familiarity with mainstream superhero comics and their characters, a wide range of readers are being exposed to the values regarding gender and social control being communicated in this genre. Therefore, it is important to assess whether social control is signified differently for males and females in superhero comics. This presentation will begin with a discussion of why it is important to study comics and graphic novels. I will then outline the concept of social control and criminological theories centered on this idea before demonstrating why superheroes may be understood as agents of social control. This will bring me to a discussion of preliminary findings of a qualitative content analysis of issues of Batman, Captain America, Wonder Woman, Ms Marvel, Justice League of America, and New Avengers comics released from May to October 2007. In examining the leadership positions, the formal and informal methods employed, those over whom social control is exercised, and relative success of each superhero in these issues, I am seeking to assess whether social control is portrayed as being exercised by male and female superheroes in different ways and to identify how this happens. The results of this content analysis will be compared with previous studies of women police officers to assess whether representations of female agents of social control in superhero comics are consistent with the experiences of real‐life criminal justice practitioners. Finally, the reasons for and implications of the nature of such media constructions will be briefly explored to encourage other students to study comics and graphic novels.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".