Intersections of age and masculinities in the information technology industry
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.012 | 0.017 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".