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Record W7139332423

Heganism

2018· article· W7139332423 on OpenAlexaff
Thomas E. Randall

Bibliographic record

VenueDigitalCommons - CalPoly (California State Polytechnic University) · 2018
Typearticle
Language
FieldEnvironmental Science
TopicAgriculture Sustainability and Environmental Impact
Canadian institutionsWestern University
Fundersnot available
KeywordsMasculinityHegemonic masculinityHegemonyAgency (philosophy)Identity (music)Consumption (sociology)
DOInot available

Abstract

fetched live from OpenAlex

An emblematic association exists between meat consumption and the gender identity hegemonic masculinity. This association is so strong that men who pursue meatless diets (especially vegans) are likely to be socially ostracized. Heganism is a diet/gender identity that aims to reconstruct hegemonic masculinity with the goal of removing these stigmas attached to male veganism. Yet heganism fails to do this, and, in fact, worsens the marginalization of male vegans. Therefore, heganism ought to be rejected. Instead, an alternative option for reducing the marginalization of male vegans could be found in the emergent literature on non-hegemonic masculinities. By rejecting hegemonic masculinity and publicizing the range of other masculine identities that exist, there is an opportunity for men to utilize their agency and interpret masculinity in their own way – especially with regards to dietary preference. In this way, focusing on non-hegemonic masculinities offers an encouraging avenue for reducing the social stigmas attached to male veganism.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.010
Threshold uncertainty score0.000

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0040.008
Scholarly communication0.0030.002
Open science0.0000.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0100.001

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 head0.008
GPT teacher head0.196
Teacher spread0.188 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2018
Admission routes1
Has abstractyes

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