Within the invisible web: Gender-based violence in agricultural streams of Canada’s Temporary Foreign Worker Program
Bibliographic record
Abstract
Canada’s agricultural sector relies heavily on labour from Temporary Foreign Agricultural Workers (TFAWs). However, TFAWs experience complex vulnerabilities resulting from structural inequalities and discrimination within Canada’s Temporary Foreign Worker Program (TFWP). Multiple, diverse, intersecting social identities of TFAWs (e.g., gender, age, race, nationality, etc.) compound, making TFAWs more or less vulnerable to gender-based violence (GBV) and discrimination in Canada and at home. This scoping review contributes to conceptual and practical knowledge regarding GBV in Canada’s agricultural TFWP. This research was guided by four objectives: (1) Collect documented evidence regarding GBV and TFAWs in the agricultural sector; (2) Describe how GBV is experienced differently by diverse groups of TFAWs in Ontario, Quebec and British Columbia; (3) Understand how policies address or confront GBV experienced by TFAWs in these three provinces; (4) Outline existing infrastructure that supports TFAWs and how supports can be enhanced to better support TFAWs who experience GBV. Using a Gender-Transformative Approach informed by Systems Thinking and Intersectionality, this study examined how structures and institutions (formal and informal) create and exacerbate inequalities between TFAW. This study found that literature on TFAWs in Canada is gender-blind, with limited discussion or reporting on GBV within the program. National and transnational policies impacting the TFWP establish and maintain structural vulnerabilities and power imbalances, making TFAWs less likely to report grievances. If workers are to be adequately protected, holistic, cohesive, and diversified support mechanisms are needed to support TFAWs’ access to rights, services and protections against GBV in Canada’s TFWP.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.018 | 0.007 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.002 | 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".