Gender and the Contours of Precarious Employment
Bibliographic record
Abstract
1. Introduction: Gender and the Concept of Precarious Employment Leah F. Vosko, Martha Macdonald and Iain Campbell 2. Canada: Gendered Precariousness and Social Reproduction Leah F. Vosko and Lisa Clark 3. The United States: Different Sources of Precariousness in a Mosaic of Employment Arrangements Francoise Carre and James Heintz 4. Australia: Casual Employment, Part-Time Employment and the Resilience of the Male-Breadwinner Model Iain Campbell, Gillian Whitehouse and Janeen Baxter 5. Japan: The Reproductive Bargain and the Making of Precarious Employment Heidi Gottfried 6. Ireland: Precarious Employment in the Context of the European Employment Strategy Julia S. O'Connor 7. The United Kingdom: From Flexible Employment to Vulnerable Workers Jacqueline O'Reilly, John Macinnes, Tiziana Nazio and Jose Roche 8. The Netherlands: Precarious Employment in a Context of Flexicurity Susanne D. Burri 9. France: Precariousness, Gender and the Challenges for Labour Market Policy Jeanne Fagnani and Marie-Therese Letablier 10. Spain: Continuity and Change in Precarious Employment John Macinnes 11. Germany: Precarious Employment and the Rise of Mini-Jobs Claudia Weinkopf 12. Sweden: Precarious Work and Precarious Unemployment Inger Jonsson and Anita Nyberg 13. Spatial Dimensions of Gendered Precariousness: Challenges for Comparative Analysis Martha Macdonald 14. Investigating Longitudinal Dimensions of Precarious Employment: Conceptual And Practical Issues Sylvia Fuller 15. Precarious Lives in the New Economy: Comparative Intersectional Analysis Wallace Clement, Sophie Mathieu, Steven Prus and Emre Uckardesler 16. Precarious Employment in the Health Care Sector
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.013 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 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".