In Search of Transdisciplinarity: A Review of Two Workshops Supported by Situating Science
Bibliographic record
Abstract
Disciplines have a way of imprisoning their creations. Entrenched in an incommensurable discourse, ideas grow stagnant. Whether ideas transcend this imprisonment is a matter of adapting, flexing, and mobilizing knowledge. This is the aim of Situating Science: Cluster for the Humanistic and Social Studies of Science. Promoting transdisciplinarity among researchers, stakeholders, and the public, the Cluster brings diverse groups of scholars to sit around a common table and discuss a common theme. My aim in this short review is to capture some of the central themes and discussions of two such workshops, one on empathy, the other evidence-based medicine. Both workshops provided a fascinating multidisciplinary perspective on topics that easily transcend disciplinary boundaries. Yet the divisions between participants were clear, leaving some discouraged about producing collaborative work. As both workshops boasted a broad range of speakers and participants, my challenge has been to identify common themes without diminishing or disregarding this multiplicity of perspectives. I have only sought to highlight some of the most thought-provoking ideas.
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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 | 0.013 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.015 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".