Using STEAM Principles and Experiences to Critique and Enhance a Transdisciplinary Collaboration Framework
Bibliographic record
Abstract
STEAM interdisciplinary collaborations are essential to address complex 21st-century challenges, as they can offer new ways of learning, knowing, and collaborating with more holistic perspectives on the world. Transdisciplinary problem framing and knowledge creation can furthermore benefit deep and applied learning and guide projects. However, setting up teams across academic disciplines and practices to achieve meaningful and effective outcomes is challenging, and a coherent framework and repeatable approach could aid in their success. Today, no framework for STEAM collaborations addresses the entire collaboration cycle between project partners from diverse academic disciplines and non-academic backgrounds to systematically achieve transdisciplinary knowledge creation. This study presents a STEAM evaluation of a general framework for conducting transdisciplinary research (TDR) in three phases: TDR initiation, TDR management, and transdisciplinary knowledge exchange. This STEAM evaluation was based on the researchers’ experience and network of STEAM research and recent projects. A particular focus in this paper is to share insights into the challenges faced by diverse disciplinary collaborations with engineering students and engineers in achieving transdisciplinarity and how a STEAM lens to a TDR framework may help to prepare for or overcome these challenges. The study is expected to contribute to the development of a comprehensive framework for STEAM collaborations that could be used by researchers and practitioners to achieve transdisciplinary knowledge creation, be that in their research or teaching.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".