The Legal In/Security of Temporary Migrant Agricultural Work: Case Studies from Canada and Australia
Bibliographic record
Abstract
Despite differing labour law systems and program structures, temporary migrant agricultural workers under the Canadian Seasonal Agricultural Worker Program and Australian Seasonal Worker Program often possess minimal security of employment rights and protections, despite potentially lengthy periods of consecutive seasonal service to the same employer. Such lesser rights and protections are partly due to the central role played by continuity of service in determining the length of reasonable notice periods and the strength of unfair dismissal protections and stand-down/recall rights. Although it is often presumed that the temporary duration of the seasonal work visa necessarily severs the legal continuity of the employment relationship, such is not the case. This article argues that security of employment rights and protections can be re-conceptualised to recognise non-continuous seasonal service within the current parameters of a fixed-term work visa. In both Canada and Australia this could be accomplished through contractual or collective agreement terms or through the amendment of labour law legislation. Such reforms would recognise a form of unpaid ‘migrant worker leave’, whereby the legal continuity of employment would be preserved despite periods of mandatory repatriation, thus allowing accrual of security of employment rights and protections.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.014 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".