Competing for hours: unstable work schedules and underemployment among hourly workers in Canada
Bibliographic record
Abstract
This is a correction notice for article bey053 (DOI: https://doi.org/10.1093/cje/bey053), published on 9 January 2019. Most recent acknowledgments For your information, in the published version, we will include the following acknowledgment: “The authors thank Statistics Canada for allowing us to use these confidential data. We specifically thank Yves Decady, Huju Liu, Douwere Grekou, and Cynthia Bocci of Statistics Canada for their help with the dataset. The University of Chicago Employment Instability, Family Well-being, and Social Policy Network (EINet), and the University of Vermont, provided financial assistance to support data access. Thanks also to Karen Messing and Susan Houseman for their comments on earlier drafts, to Samuel Bowles and Pearl Sawyer for helpful conversations, and to Alan Howard for his technical support and Lauren Meadows for research assistance.”
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.007 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".