Working time flexibilities: a paradox in call centres?, Australian Bulletin of Labour, 36 (2): 178-193
Bibliographic record
Abstract
Call centres are a source of job growth in many parts of the world.Jobs in call centres are a manifestation of the opportunities offered by ICT, together with the internal restructuring of organisations, to reduce costs and to achieve efficiencies.Extensive research has been conducted on the labour process in caii centres, with findings suggesting that the work is demanding and high-pressured, entailing continuous operations with shift work being the norm, repetition and extensive monitoring and control.Moreover, call centres often have many female operatives, linked to non-standard work arrangements and the provision of emotional skiils.Two features of call centres that are generally understated in the literature are their flat organisational structures and the use of team structures as a form of work organisation.There are often formai and Informai mechanisms that couid support flexible working arrangements, especially in the context of work-life balance issues.In this article we examine the impact of call centre work on worklife baiance.Given the evidence of a high pressure work environment, we explore the types of working time arrangements in call centres, how working hours are determined, and the impact of these hours on work-life baiance.Findings derived from a survey of 500 call centre operatives across 10 call centre workplaces and focus group interviews suggest that, despite the intensive and regulated work regimes that there is flexibiiity available in terms of adjusting working time arrangements to support non work responsibilities.A reconciiiation of these developments is considered.
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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 | 0.006 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.001 |
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".