Empirical Study of Employment Arrangements and Precariousness in Australia
Bibliographic record
Abstract
Much research on precarious employment compares permanent workers with one or two other broadly-defined employment categories. We developed a more refined method of examining precariousness by defining current employment arrangements in terms of job characteristics. These employment arrangement categories were then compared in terms of socio-demographics and self-reported job insecurity. This investigation was based on a cross-sectional population-based survey of a random sample of 1,101 working Australians. Eight mutually exclusive employment categories were identified: Permanent Full-time (46.4%), Permanent Part-time (18.3%), Casual Full-time (2.7%), Casual Part-time (9.3%), Fixed Term Contract (2.1%), Labour Hire (3.6%), Own Account Self-employed (7.4%), and Other Self-employed (9.5%). These showed significant and coherent differences in job characteristics, socio-demographics and perceived job insecurity. These empirically-supported categories may provide a conceptual guide for government agencies, policy makers and researchers in areas including occupational health and safety, taxation, labour market regulations, the working poor, child poverty, benefit programs, industrial relations, and skills development.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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".