'Work intensity' and the life course perspective: negotiating boundaries between work and life
Bibliographic record
Abstract
The concept of ‘work intensity’ has evoked considerable interest recently among policy makers and work commentators. The ‘work intensity’ literature has predominantly been researched through quantitative studies using large-scale survey instruments and it has been understood as a series of measures: the pace or speed of work, the need to meet tight deadlines and how hard or how much effort workers put into their work (European Foundation for the Improvement of Living and Working Conditions, 2001). Beyond measurements of ‘work intensity’ or work effort, as it is sometimes referred Green (2006; p.47) broadly defines ‘work intensity’ as “the intensity of labour effort during that time at work”. The paper uses face-to-face interview data collected from employees and owner/managers from 11 Australian information technology (IT) firms of the Workforce Ageing in the New Economy (WANE) project, an international project that examined employment and human resources issues in the IT sector in Australia, Canada, the United States, and the European Union. Additional data have also been used for this paper; a second face to face interview with 18 of the WANE interviewees was collected independently to the WANE project as part of a doctoral study. The research design uses a life course perspective to explore the experience of ‘work intensity’ for IT workers. The life course perspective is a framework used to understand the relationships between people’s lives and social change. It is well suited for understanding the complexity and tensions of individuals experiences combining work with their personal lives in a dynamic, global labour market. The perspective is holistic and “looks at lives in a broader context, allowing for individual agency, but understanding lives as part of a historical time and place, a series of social networks, embedded in social institutions that shape life experiences” (Marshall, 2006; p.ii). Emerging from the worker’s descriptions was the notion of pervasive work based in: the flexibility and accessibility of work; work intensive practices such as meeting deadlines and working long hours; pressures for workers to upskill and maintain current skill sets; and, managing the nature of IT work. Given the parameters of the paper a ‘snapshot’ of findings is presented for the latter two themes. The experience of ‘work intensity’ was highly variable for IT workers when contextualised in relation to the life course and the boundaries between work and personal life. This paper builds on the existing literature using a qualitative lens to provide an alternative perspective on the phenomenon of ‘work intensity’ which strongly reflects the economic and social realities of work in its current environment.
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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 | 0.008 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.007 | 0.028 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.007 |
| 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".