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Record W2731851749 · doi:10.1093/geroni/igx004.2954

MEN AND AGING: NEGOTIATING MASCULINITIY

2017· article· en· W2731851749 on OpenAlexaffabout
Michèle Charpentier

Bibliographic record

VenueInnovation in Aging · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicGender Roles and Identity Studies
Canadian institutionsUniversité du Québec à Montréal
Fundersnot available
KeywordsHegemonic masculinityMasculinityPsychologyPower (physics)NegotiationReflexivitySocial psychologyHuman sexualityHegemonyGender studiesDevelopmental psychologySociologyPolitical sciencePolitics

Abstract

fetched live from OpenAlex

This poster presents the findings of a qualitative study on men’s experience of aging that was conducted in Québec with 24 men aged 65 to 92. Their experience was examined from a subjective and reflexive (relationship to the self, body, others) point of view and based on the conduct of actors in their daily environment and the public arena (Dubet 1994). We will show that older men experience grief associated with aging, especially losses linked to the body in terms of physical aptitudes/performance, sexuality and sex appeal. They see themselves as increasingly unable to meet expectations associated with the hegemonic model of masculinity, in other words, the dominant standards and values concerning masculinity (Thompson and Wearthy 2004), which can be translated into the traditionally male qualities of emotional control, strength, and competitiveness (Roy 2008). Paradoxically, their susceptibility to the hegemonic standards of masculinity, which causes them to experience aging in terms of loss, is also what inspires these men to (re)act, and exercise what could be called their power to act. Faced with the loss of power over their body, older men develop various strategies, which change over time, to negotiate the effects of aging. Of these, the principal ones are: recreating a significant social network: volunteer work or taking on short-term jobs 2) changing reference group: comparing themselves favourably to men of their age instead of to younger men as they did before; 3) redefining the notion of age – physical age does not correspond to their mental age.

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)0.009
metaresearch head score (Gemma)0.005
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.259
Threshold uncertainty score0.515

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.001
Science and technology studies0.0260.033
Scholarly communication0.0070.004
Open science0.0010.007
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.066
GPT teacher head0.361
Teacher spread0.295 · 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 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
Published2017
Admission routes2
Has abstractyes

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