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Record W2897226261 · doi:10.1108/sbr-07-2018-0070

“Toxic Masculinity” in the age of #MeToo: ritual, morality and gender archetypes across cultures

2018· article· en· W2897226261 on OpenAlexaff
Samuel P. L. Veissière

Bibliographic record

VenueSociety and Business Review · 2018
Typearticle
Languageen
FieldNeuroscience
TopicPsychology of Moral and Emotional Judgment
Canadian institutionsMcGill University
Fundersnot available
KeywordsTrope (literature)MoralityArchetypeSociologyMasculinityVirtueGender studiesOppressionValue (mathematics)AestheticsEpistemologyPoliticsLawLiteraturePhilosophy

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.750
Threshold uncertainty score0.309

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.224
GPT teacher head0.386
Teacher spread0.162 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations63
Published2018
Admission routes1
Has abstractyes

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