Acknowledging complexity: Critically analyzing context to understand interdisciplinary research
Bibliographic record
Abstract
It is timely to develop improved understandings about strengthening interdisciplinary contexts to guide effective and quality healthcare research; contexts in which health and social issues occur do not recognize disciplinary boundaries. Similar to the notion of "partnership", the terms multidisciplinary, interdisciplinary and transdisciplinary are in danger of becoming conceptually indistinct and thus of limited usefulness for researchers, practitioners and teams. In this paper, we review basic concepts related to cross-disciplinary relationships as well as common arguments for and against interdisciplinary research. We then extend this critique by adding considerations of the influence of context, specifically social and spatial influences on interdisciplinarity. In doing so, we advocate the need for research that explicitly acknowledges complexity and considers context to advance understanding of effective interdisciplinary research.
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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 | 0.105 | 0.150 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.012 | 0.007 |
| Science and technology studies | 0.016 | 0.096 |
| Scholarly communication | 0.032 | 0.044 |
| Open science | 0.007 | 0.030 |
| Research integrity | 0.009 | 0.017 |
| 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".