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Record W2117582807 · doi:10.1017/s0144686x06005605

Intersections of age and masculinities in the information technology industry

2007· article· en· W2117582807 on OpenAlexafffundabout
Tammy Duerden Comeau, Candace L. Kemp

Bibliographic record

VenueAgeing and Society · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicDigital Games and Media
Canadian institutionsWestern University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsMasculinityGender studiesStereotype (UML)Context (archaeology)WorkforceSociologyWork (physics)PerceptionSocial psychologyPsychologyPolitical scienceEngineeringGeography

Abstract

fetched live from OpenAlex

This paper explores the intersections of age and masculinities in small information technology (IT) firms in Canada. The IT workforce, although demographically young, does not entirely comprise younger workers but is dominated by men and is ageing. Despite the infamous ‘nerd’ stereotype of IT workers and its associations with immature age and masculinity, perceptions of age and ageing in the industry have not been considered in the context of masculinities. To what extent are conceptualisations of IT work shaped by notions of age and masculinities? How do perceptions of age and masculinities correspond to occupational trajectories and responsibilities in IT work settings? To address these questions, this paper reports an analysis of qualitative semi-structured interviews with 76 employees of small IT firms in Canada. The findings indicate that the dominant frameworks for describing the nature of IT work are metaphors and analogies with sports, the military, entrepreneurial drive and craftsmanship. This paper focuses on the allusions to sport, war and ‘being driven’, and argues that the discursive ties to these masculine arenas normalise, or make ‘natural’, the affiliation of youthfulness and technical ability. The corresponding intersections between age and masculinity suggest that older workers are marginalised in small IT firms.

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)0.000
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 score0.208
Threshold uncertainty score0.106

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 head0.015
GPT teacher head0.275
Teacher spread0.260 · 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

Citations31
Published2007
Admission routes3
Has abstractyes

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