Serendipity during the pandemic: Taking a community-partnered study about young, forced migrants online
Bibliographic record
Abstract
This research update describes the transformation of a partnership project between a university-based team in Canada and a migrant-serving community organization in Thailand occasioned by the pandemic. Travel restrictions preventing the Canada-based team from carrying out project activities directly with young, forced migrants provided the impetus to explore an entirely online collaboration over 18 months. This shift flattened what would likely have been a hierarchical role structure, with the Canada-based team members positioned as experts and primary actors in conducting the project. The partners deliberated together about the cultural fit, desirability, feasibility and potential variations of the novel Peer Mediated Story Board Narrative method, which is intended both as a means of data collection and an intervention for migrant youth needing psychosocial support. In consultation with the Canada-based team, the Thailand-based partners undertook participant recruitment and piloted the method with diverse groups of migrant youth living in Myanmar and Thailand, using creative approaches including conducting the method online with groups of youth using smart phones. The serendipitous benefit of moving the partnership online highlights the potential for a more probing, mutually interdependent, less costly collaboration in which partners enter into an ethical space between partners’ worlds. In this space, assumptions, core constructs, and methodological fidelity can be challenged, new understandings can be forged and, in the case of this project, a sustainable approach to psychosocial support for forced migrant youth can be co-created.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.021 | 0.007 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".