Barriers to Research Collaboration across Disciplines: Scientific Paradigms and Institutional Practices
Bibliographic record
Abstract
In a recent paper in this journal it was suggested that the conventional knowledge practices of disciplines are the fundamental obstacle to mutual understanding between academic experts. Such a position, we argue, underplays the institutional relationships that recreate expert and disciplinary divides. To demonstrate our case we discuss how in the UK the evolving relationship between the government and research councils has crucially altered the context for efforts to stimulate interdisciplinary research. Our analysis highlights the scope for changes in institutional structures and practices that would facilitate broader and more encompassing research into complex sustainability problems.
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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 | 0.415 | 0.463 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.019 | 0.068 |
| Scholarly communication | 0.047 | 0.032 |
| Open science | 0.010 | 0.046 |
| Research integrity | 0.012 | 0.012 |
| Insufficient payload (model declined to judge) | 0.004 | 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".