Negotiating the Complexities and Risks of Interdisciplinary Qualitative Research
Bibliographic record
Abstract
This article interrogates the experiences of an interdisciplinary research team that engaged in a qualitative research program for over 5 years, beginning with the grant writing process through to knowledge dissemination. We highlight the challenges of constructing shared understanding and developing research synergies, embracing vulnerability and discomfort to advance knowledge, and negotiating risks of legitimacy and transcending disciplinary boundaries. Based on critical reflections from the research team, the findings call attention to the politics of knowledge production, the internal and external obstacles, and the open mindedness and emotional sensitivity necessary for interdisciplinary qualitative research. Emphasis is placed on relational and structural processes and mechanisms to negotiate these challenges and the potential for interdisciplinary research to enhance the significance of scholarly 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 | 0.605 | 0.484 |
| Meta-epidemiology (narrow) | 0.002 | 0.004 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.045 | 0.098 |
| Scholarly communication | 0.037 | 0.030 |
| Open science | 0.009 | 0.060 |
| Research integrity | 0.010 | 0.016 |
| 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".