Negotiating the politics of identity in an interdisciplinary research team
Bibliographic record
Abstract
This article explores the politics of identity in an interdisciplinary health research team that has been engaged in a qualitative research program for over five years. We draw on sociological theories of power and knowledge to explore our experiences of identity conflict, team socialization, and knowledge production. Structurally, our article integrates individual and group perspectives through personal narratives and collaborative critique as we explore the complex negotiations required to realize and maintain our team dynamic. These negotiations take place not only with one another as particularly positioned individuals, but also with the ideological and organizational forces that structure our scholarly worlds. We conclude with articulating `lessons learned' that we hope will enable other interdisciplinary research teams to realize the rich potential of their collaborative qualitative work.
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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 | 0.125 | 0.091 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.037 | 0.072 |
| Scholarly communication | 0.028 | 0.019 |
| Open science | 0.003 | 0.025 |
| Research integrity | 0.006 | 0.007 |
| Insufficient payload (model declined to judge) | 0.002 | 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".