Integrated Creative Practices (ICP) for transdisciplinary research and knowledge mobilization
Bibliographic record
Abstract
Creative practices have long fueled innovation and insight in the sciences and beyond, yet there remains no widely adopted framework for integrating creative practices into transdisciplinary research. In response, we introduce the Integrated Creative Practices (ICP) framework, grounded in a transdisciplinary research project that addressed a complex environmental challenge. The ICP framework offers a pathway for researchers from the arts and sciences to collaborate with non-academic stakeholders in transdisciplinary research without sacrificing disciplinary rigor or practical outcomes. By leveraging design methods and creative practices, the framework facilitates collaboration across the arts and sciences while bridging the gap between knowledge and its mobilization. As a future-oriented discipline positioned at the intersection of the arts and sciences, design offers a unique set of tools, frameworks, and methods that are well-suited for addressing complex problems that require multifaceted solutions, robust stakeholder engagement, and iterative, non-linear approaches. Moreover, the inherently dialogic, participatory, and socially-oriented aspects of design methods and practices are uniquely well-suited to facilitating transdisciplinary collaborations that bridge the knowledge-to-action gap limiting the impact of academic 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 | 0.071 | 0.046 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.008 | 0.005 |
| Science and technology studies | 0.010 | 0.058 |
| Scholarly communication | 0.022 | 0.015 |
| Open science | 0.006 | 0.031 |
| Research integrity | 0.008 | 0.007 |
| Insufficient payload (model declined to judge) | 0.009 | 0.002 |
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".