Flawed by Design?: A Case Study of Federal Enforcement of Migrant Workers’ Labour Rights in Canada
Bibliographic record
Abstract
Although Canada's migrant labour programs are seen by some as models of best practices, rights shortfalls and exploitation of workers are well documented. Through migration policy, federal authorities determine who can hire migrant workers and the conditions under which they are employed, through the provision of work permits. Despite its authority over work permits, the federal government has historically had little to do with the regulation of working conditions. In 2015, the federal government introduced a new regulatory enforcement system -unique internationally for its attempt to enforce migrants' workplace rights through federal migration policy -under which employers must comply with contractual employment terms, uphold provincial workplace standards, and make efforts to maintain a workplace free of abuse. Drawing on enforcement data, and frontline law and policy documents, we critically assess the new enforcement system, concluding that, because of design flaws and implementation failures, it does not realize its potential to protect workers' rights.
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 | 0.015 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.048 | 0.012 |
| Scholarly communication | 0.007 | 0.002 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.007 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".