Play-making with migrant farm workers in Ontario, Canada: a kinesthetic and embodied approach to qualitative research
Bibliographic record
Abstract
This article is a reflection on the use of theatre creation in qualitative research with migrant farm workers in Ontario, Canada. In this article I examine how the fundamentally embodied and kinesthetic dimensions of seasonal agricultural workers’ lives in Canada highlight the need to seek out and develop corresponding embodied approaches that are able to access and accurately represent the fraught and dynamic nature of workers’ experiences. I bring together ideas from both arts-informed research and participatory action research, and I examine how engaging research participants directly in collective theatre creation can effectively disrupt accepted ways of being and offer an important intervention on worker habitus. I reflect on how through incorporating an element of play-creation in the qualitative research process, I was able to a) access forms of knowledge that may otherwise have remained tacit and b) offer a disruption of the norms of isolation and antagonism endemic to daily life in Canada’s Seasonal Agricultural Worker Program. This article contributes to debates concerning the role of the arts in qualitative and action research, as well as to those researchers who are seeking innovative ways of designing and implementing qualitative research in the areas of precarious work and citizenship.
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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 | 0.011 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.021 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".