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Record W3212998279

Media, Masculinities and Other Interpretive Frameworks: Reflecting on Audience, Representation, Bodies and Mark Moss’ The Media and Models of Masculinity

2016· article· en· W3212998279 on OpenAlexaboutno aff
Clifton Evers

Bibliographic record

VenueDergiPark (Istanbul University) · 2016
Typearticle
Languageen
FieldSocial Sciences
TopicMedia, Gender, and Advertising
Canadian institutionsnot available
Fundersnot available
KeywordsMasculinityRepresentation (politics)MossSociologyGender studiesPolitical sciencePolitics
DOInot available

Abstract

fetched live from OpenAlex

asculinity is not fixed, natural, or immutable.Frank mort explains that masculinity is always in process (196).Raewyn Connell argues that there are multiple masculinities functioning at any given time.These masculinities are not types but patterns of practice and meaning structured by social, historical, and cultural conditions.David Buchbinder instructs that masculinities are relational and derive from each other (as well as femininities) their meanings, practices, values and significance.And Lynne Segal asserts that they are subject to change.It has been argued by Raewyn Connell that there is a hegemonic masculinity, an ideal rather than a reality, specific to particular cultural, social and historical settings.Given this, some masculinities are subordinated, marginalized, and work to protest the hegemony and others.These masculinities overlap and are not mutually exclusive.Mark Moss concludes that there are now more variations than ever before. Mark Moss' (2011) book The Media and the Models of Masculinitybegins from the social-constructionist perspective of gender to provide a historical account of how various models of masculinity are "conditioned, defined, or illustrated by different media" (179).He provides examples of how hegemonic masculinity is repeatedly verified through particular models of masculinity in the media.The focus in this book is on the U.S. and Canadian social, cultural and historical context.It M

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)0.001
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 score0.143
Threshold uncertainty score0.575

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.002
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 head0.065
GPT teacher head0.316
Teacher spread0.250 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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