Contingent Employment and Labour Market Pathways: Bridge or Trap?
Bibliographic record
Abstract
The debate over whether contingent (and typically more precarious) employment acts as a bridge to permanent employment, or as a trap, has tended to focus on transitions rather than longer-run pathways. This approach cannot accurately identify indirect pathways from contingent to permanent employment or ‘trap’ pathways involving short spells in other states. It also fails to distinguish between those experiencing contingent employment as a ‘blip’ and those with longer spells. This article employs a different approach involving sequence analysis. Exploiting longitudinal data for Australia, evidence for the co-existence of pathways that correspond to ‘bridge’ and ‘trap’ characterizations of contingent employment is found. Further, in the case of casual employment—the most common form of contingent employment in Australia—these two types of labour market pathways are roughly equally prevalent, although for women and those with low educational attainment ‘traps’ are more likely than ‘bridges’.
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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 | 0.005 |
| 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 | 0.004 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".