Prolonging the careers of older information technology workers: continuity, exit or retirement transitions?
Bibliographic record
Abstract
ABSTRACT The article explores the ways in which older workers' career trajectories influenced their exit from or continuity of employment in the Australian information technology (IT) industry. The data were collected through qualitative interviews with 71 employees of 10 small and medium-sized IT firms as part of the cross-countryWorkforce Ageing in the New Economyproject (WANE), which was conducted in Canada, the United States, Australia and several European Union countries (the United Kingdom, Germany and The Netherlands). The analysis revealed that older IT workers' capacity to envisage careers beyond their fifties was constrained by age-based ‘normative’ capability assumptions that resulted in truncated careers, dissuaded the ambition to continue in work, and induced early retirement. The workers' constricted, age-bound perspectives on their careers were reinforced by the rapid pace of technological and company transformations. A structural incompatibility was found between the exceptional dynamism and competitiveness of the IT industry and the conventional age-staged and extended career. The analysis showed that several drivers of occupational career trajectories besides the well-researched health and financial factors predisposed ‘default transitions’ to exit and retirement. The paper concludes with policy and practice recommendations for the prolongation of IT workers' careers and their improved alignment with the contemporary lifecourse.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".