Precarious work: recent research and policy challenges
Bibliographic record
Abstract
The past three and half decades have witnessed profound and global changes in the organisation of work. The ‘standard’ employment model of the post-1945 period ‐ whereby most workers (though not most women workers) within Europe, North America and Australasia held ongoing jobs ‐ went into decline. In its place emerged what is often labelled the ‘new world of work’. We will not use this term because some of the work arrangements seen to be new actually represent the re-emergence of far older types of work organisation. 1 That matter aside, the changes that this term sought to describe were marked by the growth of more temporary work arrangements (fixed-term contracts, direct-hire temporary work, temporary agency work, on-call and casual work), self-employment and work remote from formal workplaces (including various forms of remote and home-based work, such as homecare). Further, even workers retaining notionally permanent jobs increasingly experienced insecurity as the result of repeated rounds of downsizing/restructuring and outsourcing/offshoring of activities by large private and public sector employers (in the public sector, these changes were also often associated with privatisation). In short, the growth of ‘flexible’ work arrangements marked by more irregular working hours and limited tenure has essentially made the jobs of a growing body of workers precarious. In tandem with this trend has been the growth of informal or undeclared work arrangements, often using migrant/undocumented workers, in Europe, North America and elsewhere. Since the 1990s, a rapidly growing body of international research has indicated that job insecurity and precarious work arrangements are associated with significant adverse effects on health and safety outcomes, as measured by the incidence of injury, catastrophic events (such as the AZF factory fire in Toulouse in 2001), exposure to hazardous substances, occupational violence, psychological distress, work‐life imbalance and non-compliance with workplace safety rules and regulations. 2‐9
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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 | 0.033 | 0.050 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.014 |
| Science and technology studies | 0.006 | 0.019 |
| Scholarly communication | 0.022 | 0.024 |
| Open science | 0.007 | 0.009 |
| Research integrity | 0.018 | 0.016 |
| Insufficient payload (model declined to judge) | 0.028 | 0.002 |
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".