The Key to Fostering Transdisciplinary Research Collaboration: Finding the Connections
Bibliographic record
Abstract
How to foster collaboration among researchers is an enduring issue facing university administrators. Homophily, the tendency that people like to work with those similar to themselves, has been found as a fundamental mechanism behind scientific collaboration. When similarity breeds connections, collaboration becomes conceivable. In a multidisciplinary context, like a university, how can a homophily mechanism be applied to facilitate research collaboration? This paper proposes a co-word analysis approach which can be used to process a large volumes of data. The aim is to identify and reveal hidden connections between university faculty members and contribute towards cultivating transdisciplinary research collaboration.Comment favoriser la collaboration entre chercheurs est un problème permanent auquel sont confrontés les administrateurs universitaires. L’homophilie, la tendance selon laquelle les gens aiment à travailler avec ceux qui leur ressemblent, a été reconnue comme un mécanisme fondamental derrière la collaboration scientifique. Lorsque la similarité engendre des connexions, la collaboration devient concevable. Dans un contexte multidisciplinaire, comme une université, comment un mécanisme d'homophilie peut-il être appliqué pour faciliter la collaboration en recherche? Cet article propose une approche d'analyse de co-occurence de termes qui peut être utilisée pour traiter de gros volumes de données. L'objectif est d'identifier et de révéler les connexions cachées entre les membres du corps professoral des universités et de contribuer à cultiver la collaboration de recherche transdisciplinaire.
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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 | 0.020 | 0.081 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.013 | 0.020 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".