Teaching Villainification in Social Studies
Bibliographic record
Abstract
In this collection, scholars from the United States, Canada, and Australia examine the concepts of villainification and anti-villainification in social studies curriculum, popular culture, as well as within sociocultural contexts and their implications. Villainification is the process of identifying an individual or a small group of individuals as the sole source of a larger evil. Anti-villainification considers the messy space in between individual and group culpability in order to help students develop a sense of responsibility to each other as humans in communities on this planet. Chapter authors examine topics related to U.S. politics, financial education, Holocaust education, difficult histories, apocalypse fiction, the Marvel Cinematic Universe, technology use, LGBTQ school experiences, rape culture, geographies of invasion, and the female body. Taken together, these inquiries into villainification offer thoughtful and powerful insights for teaching about historical wrongdoing in more nuanced ways, addressing the responsibility we all have to create a better world. Contributors: Heather P. Abrahamson • Danelle Adeniji • Erin C. Adams • Rebecca C. Christ • Brandon Haas • Keri Helgren • Brittany L. Jones • Wayne Journell • Daniel G. Krutka • Melissa McQueen • Bryan Smith • Ryan M. Smits • Oren Baruch Stier • Amanda Thomson • Andrew Thomson • Bretton A. Varga Book Features: Pushes the field of social studies to develop a more nuanced understanding of the villains of the past and present. Invites educators to become more thoughtful about not only curriculum but also the world around us. Helps readers to more deeply understand how easily forms of banal evil can touch our lives within and beyond the classroom, and what we might do about it. Examines how systemic forces can influence “average” individuals to cause or contribute to great societal harm. Includes teacher-friendly engagements with theory, using examples from middle and high school classrooms. Offers a wide range of contexts related to social studies education, including civics, economics, geography, and history. “Encourages educators and students in the context of social studies education to delve deeper into exploring the nuanced aspects of contemporary and historical forms of evil.” —From the Foreword by Michalinos Zembylas, professor, Open University of Cyprus
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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 | 0.007 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.018 | 0.044 |
| Scholarly communication | 0.009 | 0.008 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".