The Methods and Meanings of Collaborative Team Research
Bibliographic record
Abstract
Team research enables the collection of multiple, sometimes conflicting, stories of migration, family, and belonging. Using common qualitative methods within a team research context can stretch these research techniques in productive and instructive ways and proffer new insight and meaning.Therefore, the authors suggest that team research offers an important avenue for both extending qualitative methods and expanding interpretative lenses. To illustrate these points, the authors draw upon their study of the settlement and migration patterns of East African Shia Ismaili Muslims in Vancouver, British Columbia, Canada, and discuss their experiences with focus group effects, the simultaneous household interview strategy, and postinterview dialogues. The article highlights how these three techniques and effects enacted in the team research context helped the authors explicitly locate contradictions, ambiguities, and paradoxes within the narratives of first- and second-generation Ismailis.
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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 | 0.102 | 0.083 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.009 | 0.011 |
| Science and technology studies | 0.010 | 0.057 |
| Scholarly communication | 0.022 | 0.011 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".