Bibliographic record
Abstract
Complex research challenges facing society today require an integrative approach, therefore, interdisciplinary research is now required more often. By creating interdisciplinary research communities, we facilitate communication, collaboration, and knowledge sharing between researchers from different fields. It can however be difficult to create interdisciplinary communities within universities, but co-design methods have been seen as being beneficial in doing so. Reporting and reflecting on three case studies (including N=130 participants), this paper aims to explore the use of co-design methods in creating interdisciplinary research communities In this paper, we focus on two main characteristics of co-design workshops. 1. Design/ Scheduling and Planning and 2. Workshop Formats, specifically co-design canvases. In doing so it seeks to 1. Offer a report and reflection on the three different co-design workshop approaches informing future co-design research and practice. 2. Understand how different formats of co-design help enhance interdisciplinary research communities in universities. It found that there were trade-offs in selecting approaches. Structured co-design approaches offer clear expectations and organisation but may limit creativity, while semi-structured approaches provide flexibility but may lead to reduced focus. Similar trade-offs were seen in the differing fidelities of canvas design. Low-fidelity canvases are inclusive but may lack detail, while high-fidelity canvases may limit creativity. Medium-fidelity canvases strike a balance between visual appeal and detail. It was found the best approach depends on the specific context and goals of participants; therefore, it is important to prepare in advance to tailor workshops to the needs and preferences of the participants involved.
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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 | 0.075 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.004 | 0.015 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".