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 people 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. Using a situational analysis research approach, we carried out 4 focus groups and 25 one-on-one interviews, recruiting a total of 30 informal and formal support people 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 organising them in terms of situations, social worlds, and discursive positions. Ultimately, we identified four best practices: (a) Anticipating and addressing barriers; (b) building trust and community; (c) acknowledging rights and system accountability and (d) bearing witness and looking to the future. Underlying these best practices was the need for support people to display 'support readiness', or specialised 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 people will be unable to fill the gaps in support for this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".