Teaching Beyond the Screen: How Do Teachers Combat Online Misogyny Amongst Adolescent Boys?
Bibliographic record
Abstract
Concerns about the influence of misogynistic social media content on adolescent boys have become increasingly urgent in U.S. education, yet little research has examined how American high school teachers are responding to this growing epidemic. While studies from Australia, the United Kingdom, and Canada have explored school-based responses to online misogyny and sexist behavior, this thesis addresses a significant gap in the U.S. context. Based on 17 in-depth interviews with high school teachers in the Chicagoland area, this study investigates how educators perceive the influence of popular male social media influencers ("Manfluencers") amongst boys, what they observe in the classroom, and how they respond. While most teachers acknowledged that online misogyny was shaping boys' behavior and beliefs, their responses varied dramatically, shaped less by any shared framework or school policy than by their own identities, pedagogical orientations, and institutional constraints. Teachers differed on whether they saw misogyny as widespread or isolated, on their ability to respond, or whether it was part of their teaching responsibility to act at all. This variation reveals a deeper absence of institutional coordination as well as a lack of consensus about the nature of the problem itself. While some teachers proposed mandatory gender curricula or other types of interventions, others avoided engagement altogether. Beyond a simple binary of punitive versus restorative responses, this thesis argues teachers are navigating a broader landscape of uncertainty—one marked by unclear expectations and uneven support. Addressing this gap demands not just better resources, but a whole-school reorientation grounded in care, community, and social justice.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".