Taught to Hate, Longing to Belong: Misogyny and the Making of Masculinity in <i>Adolescence</i>
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 it