Taught to Hate, Longing to Belong: Misogyny and the Making of Masculinity in <i>Adolescence</i>
Bibliographic record
Abstract
This article examines the Netflix series Adolescence (2025) to explore how misogynistic ideologies influence the formation of masculinity during adolescence, emphasizing themes of hate, belonging, and digital socialization. Through narrative inquiry and cinema therapy lenses, the analysis reveals the profound psychological impacts of online misogyny and peer victimization, underscoring the dangerous allure of belonging that extremist digital communities offer vulnerable young males. Drawing upon experiences from working in juvenile detention centers, the authors highlight the ethical imperative to authentically represent marginalized adolescent narratives. Additionally, the article addresses systemic gaps in parental awareness, institutional accountability, and societal preparedness to mitigate these digital risks. Concluding with recommendations for integrated clinical, educational, and policy-based interventions, this article calls for collective action to foster healthier masculinities, emotional resilience, and digital literacy among adolescents navigating complex online landscapes.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".