Diverse perspectives on interdisciplinarity from Members of the College of the Royal Society of Canada
Bibliographic record
Abstract
Various multiple-disciplinary terms and concepts (although most commonly “interdisciplinarity,” which is used herein) are used to frame education, scholarship, research, and interactions within and outside academia. In principle, the premise of interdisciplinarity may appear to have many strengths; yet, the extent to which interdisciplinarity is embraced by the current generation of academics, the benefits and risks for doing so, and the barriers and facilitators to achieving interdisciplinarity, represent inherent challenges. Much has been written on the topic of interdisciplinarity, but to our knowledge there have been few attempts to consider and present diverse perspectives from scholars, artists, and scientists in a cohesive manner. As a team of 57 members from the Canadian College of New Scholars, Artists, and Scientists of the Royal Society of Canada (the College) who self-identify as being engaged or interested in interdisciplinarity, we provide diverse intellectual, cultural, and social perspectives. The goal of this paper is to share our collective wisdom on this topic with the broader community and to stimulate discourse and debate on the merits and challenges associated with interdisciplinarity. Perhaps the clearest message emerging from this exercise is that working across established boundaries of scholarly communities is rewarding, necessary, and is more likely to result in impact. However, there are barriers that limit the ease with which this can occur (e.g., lack of institutional structures and funding to facilitate cross-disciplinary exploration). Occasionally, there can be significant risk associated with doing interdisciplinary work (e.g., lack of adequate measurement or recognition of work by disciplinary peers). Solving many of the world’s complex and pressing problems (e.g., climate change, sustainable agriculture, the burden of chronic disease, and aging populations) demands thinking and working across long-standing, but in some ways restrictive, academic boundaries. Academic institutions and key support structures, especially funding bodies, will play an important role in helping to realize what is readily apparent to all who contributed to this paper—that interdisciplinarity is essential for solving complex problems; it is the new norm. Failure to empower and encourage those doing this research will serve as a great impediment to training, knowledge, and addressing societal issues.
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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 | 0.027 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.072 | 0.052 |
| Scholarly communication | 0.034 | 0.007 |
| Open science | 0.004 | 0.013 |
| Research integrity | 0.007 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 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; 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".