Still methodologically becoming: collaboration, feminist politics and ‘Team Ismaili’
Bibliographic record
Abstract
This article mobilizes a feminist analytic to examine team research and collaborative knowledge production. We center our encounter with team research – a collectivity we named ‘Team Ismaili’ – and our study with first- and second-generation East African Shia Ismaili Muslim immigrants in Greater Vancouver, Canada. We draw upon feminist politics to highlight the ways in which ‘Team Ismaili’ at once destabilized and unwittingly reproduced normative academic power relations and lines of authority. A ‘backstage tour’, of ‘Team Ismaili’ shows the messiness and momentum of team research and sheds light on how collaborative knowledge production can challenge and reconfirm assumed hierarchies. Even as we are still methodologically becoming, through this discussion we strive to interrupt the prevailing silence on team research in human geography, to prompt more dialogue on collaboration and to foreground the insight garnered through feminist politics.
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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 | 0.062 | 0.053 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.026 | 0.075 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.005 |
| 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".