Bibliographic record
Abstract
If you believed most of what’s said about the Canadian Temporary Foreign Worker program, you might naturally assume that there is a trade-off between workers’ poor experiences with the program and employers’ significant benefits. In reality, the experiences of workers are far worse than is commonly acknowledged, while employers are not reaping as much benefit as the public might suppose. In Enduring Work Catherine Connelly draws on over one hundred interviews with people connected to different aspects of this program, analyzing their experiences from the perspective of organizational behaviour and human resources management. She compares the lived reality of agricultural workers, in-home caregivers, and low- and high-wage workers, showing how and why each group is vulnerable to mistreatment, albeit in different ways. She further explores how employment agencies and immigration consultants contribute to program abuses. Critically, Enduring Work provides the perspectives of employers, distinguishing between the reluctant users of the program who follow the rules and the reckless users who do not. Groundbreaking in its analysis of an issue very much in the news, Enduring Work unpacks the harms within Canada’s Temporary Foreign Worker program and offers nuanced strategies to improve it.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.007 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.049 | 0.013 |
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".