“Toxic Masculinity” in the age of #MeToo: ritual, morality and gender archetypes across cultures
Bibliographic record
Abstract
Purpose This paper aims to take the “toxic masculinity” (TM) trope as a starting point to examine recent cultural shifts in common assumptions about gender, morality and relations between the sexes. TM is a transculturally widespread archetype or moral trope about the kind of man one should not be. Design/methodology/approach The author revisits his earlier fieldwork on transnational sexualities against a broader analysis of the historical, ethnographic and evolutionary record. The author describes the broad cross-cultural recurrence of similar ideal types of men and women (good and bad) and the rituals through which they are culturally encouraged and avoided. Findings The author argues that the TM trope is normatively useful if and only if it is presented alongside a nuanced spectrum of other gender archetypes (positive and negative) and discussed in the context of human universality and evolved complementariness between the sexes. Social implications The author concludes by discussing stoic virtue models for the initiation of boys and argues that they are compatible with the normative commitments of inclusive societies that recognize gender fluidity along the biological sex spectrum. Originality/value The author makes a case for the importance of strong gender roles and the rites and rituals through which they are cultivated as an antidote to current moral panics about oppression and victimhood.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".