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Record W1947767833

Conceptualizing Masculinity in Female-to-Male Trans-Identified Individuals: A Qualitative Inquiry

2013· article· en· W1947767833 on OpenAlexvenueno aff
Vanessa Vegter

Bibliographic record

VenueCanadian Journal of Counselling and Psychotherapy · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityFemininityIdentity (music)NormativeRealmPsychologySocial psychologyEmbodied cognitionGender studiesQualitative researchGender schema theorySexual identitySet (abstract data type)Male femaleDevelopmental psychologySociologyHuman sexualityEpistemologySocial science
DOInot available

Abstract

fetched live from OpenAlex

A non-normative gender identity raises questions concerning widely accepted theories of gender that prevail in Western society. These theories are founded upon dichotomous models of gender identity that are posited as having a direct relationship to binary biological sex. The purpose of this qualitative study was to explore how individuals who have transitioned from female to male (FTM) conceptualize their masculinity outside of the constraints of the binary model. Six FTM participants who had transitioned to some degree were interviewed. Through the exploration of the participants’ lived experience and understanding of their male identities, 5 major categories, 12 major themes, and 48 subthemes emerged. A process entitled Embodying a Male Identity was revealed. According to this process, the FTMs in this study embodied a male identity through a variety of experiences that serve to align external physiology with internal self. This process suggests that masculinity, which is often interpreted in the social realm as a validation of maleness, is not a requirement for, or a product of, a male gender identity. Rather, masculinity (alongside femininity) is viewed by participants as a set of traits that vary naturally in all humans (regardless of gender).

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)0.002
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: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.340
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
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 head0.095
GPT teacher head0.376
Teacher spread0.281 · 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 designQualitative
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

Citations12
Published2013
Admission routes1
Has abstractyes

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