From ‘villains’ to ‘idols’: exploring teenage boys’ conflicting attachments to manospheric masculinities
Bibliographic record
Abstract
Andrew Tate is emblematic of a new iteration of male supremacist influencers, often referred to as ‘manfluencers’ or ‘misogyny influencers’. Research on the relationship between boys’ consumption of and support for these influencers, and their circulation of regressive gender ideologies, is in its formative stages. This paper explores these issues using focus groups and follow-up interviews with young people (aged 12–17) in four schools in London, England. While many boys ‘othered’ Andrew Tate and condemned his misogyny, several boys simultaneously demonstrated a continuum of support for manospheric content promoting sexist gender roles and exaggerated masculine ideals. We discuss how boys affectively responded to these digital discourses and the role of humour in normalizing the gendered power hierarchies at play. The findings highlight the need for interventions that go beyond condemning single influencers, situating manospheric masculine archetypes within the systemic and increasingly networked subordination of women, femininity, and Queer identities on mainstream platforms.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.009 | 0.015 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".