Emerging best practices for supporting temporary migrant farmworkers in Western Canada
Bibliographic record
Abstract
The aim of this study was to examine the role of support persons in determining migrant agricultural workers’ access to, or ability to navigate, public spaces and services. While the role of support networks for this population is still in its infancy, much can be gained from understanding the emerging best practices for helping this group. To refine our research design, we first carried out public consultations with hundreds of migrant agricultural workers (n = 234) and a community scan with various front-facing service agencies (n = 28) in 2018. Then, using a situational analysis research approach, we carried out 4 focus groups and 25 one-on-one interviews, recruiting a total of 35 informal and formal support persons as study participants between 2018 and 2019. Data analysis occurred over a 2-year period largely simultaneously with data collection. Developing analytic maps as outlined by Clarke’s approach to situational analysis, we reviewed texts and preliminary codes by organizing them in terms of situations, social worlds, and discursive positions. Ultimately, we identified 4 best practices: (1) Anticipating and addressing barriers; building trust and community; (3) acknowledging rights and system accountability; (4) bearing witness and looking to the future. Underlying these best practices was the need for support persons to display ‘support readiness,’ or specialized skills, motivation and a personal connection to migrant farmworkers. While these practices have the potential to improve migrant workers’ ability to fully participate in public spaces and access public services, until systemic constraints are addressed, support persons will be unable to fill the gaps in support for this population.
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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 | 0.008 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.011 | 0.003 |
| Open science | 0.005 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| 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".