Making Interdisciplinary Collaboration Work: Key Ideas, a Case Study and Lessons Learned
Bibliographic record
Abstract
This article discusses the ‘lessons learned’ from an attempt to establish an interdisciplinary education research group. The growth, development and dissolution of the group are treated as an instrumental case study. Current literature on interdisciplinary collaboration is synthesized in order to provide a frame for analysis. Data was collected over several years and included three rounds of written participant reflections and documentation of group activities and meetings. Five major themes arose from the research, covering issues such as disciplinary diversity, common ground, interpersonal relationships, career pressures, and the need for concrete problems and tangible progress. Based on these themes, a number of ‘lessons learned’ are discussed which will likely be of great interest to those considering similar interdisciplinary initiatives.
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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 | 0.053 | 0.038 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.020 | 0.026 |
| Scholarly communication | 0.020 | 0.028 |
| Open science | 0.007 | 0.015 |
| Research integrity | 0.015 | 0.012 |
| Insufficient payload (model declined to judge) | 0.002 | 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".