Mapping SDGs by Faculty to Find New Interdisciplinary Collaborations, a Type of Linked Literature Analysis
Bibliographic record
Abstract
Leveraging the Dimensions bibliographic database, publications that have been assigned to SDG categories in Dimensions are matched by DOI to their authors' faculty affiliations in the Research Information Management System of McMaster University. This gives an SDG-by-Faculty profile of research at McMaster University. Moreover, inter-faculty collaborations can be identified in each SDG category. This gives management a picture of the collaboration patterns across campus and to understand the broader impact of inter-faculty collaborations. Intriguingly, the same metadata can be recombined to identify which faculty (who have not previously co-authored together) could collaborate on new research. Essentially, researchers from different faculties may be working in closely-related topics unbeknownst to each other. By combining data-analytical techniques with the domain knowledge of academic leadership this approach helps to overcome institutional silos, enabling management to be proactive by fostering new inter-faculty collaborations in order to investigate specific topics within a given SDG. Cartographier les ODD par faculté pour trouver de nouvelles collaborations interdisciplinaires, un type d'analyse de la littérature liée RésuméEn exploitant la base de données bibliographiques Dimensions, les publications qui ont été assignées aux catégories ODD dans Dimensions sont appariées d'un DOI qui rejoint la faculté affiliée des auteurs dans le système de gestion de l'information sur la recherche de l'Université McMaster. On obtient ainsi un profil de la recherche à l'Université McMaster par ODD et par faculté. En outre, les collaborations interfacultés peuvent être identifiées dans chaque catégorie d'ODD. Cela permet à la direction de se faire une idée des modes de collaboration sur le campus et de comprendre l'impact des collaborations interfacultés. Il est intéressant de noter que les mêmes métadonnées peuvent être recombinées pour identifier les facultés, qui n'ont jamais collaboré, qui pourraient collaborer à de nouvelles recherches. En fait, des chercheurs de différentes facultés peuvent travailler à leur insu sur des sujets étroitement liés. En combinant des techniques d'analyse de données avec la connaissance du domaine des leaders académiques, cette approche aide à surmonter les silos institutionnels, permettant ainsi à la direction d'être proactive en favorisant de nouvelles collaborations interfacultés pour étudier des sujets spécifiques dans le cadre d'un ODD donné. Mots-clésBibliothéconomie universitaire; bibliométrie; découverte; interaction humain-machine
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.087 | 0.109 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".