Mapping publications by sustainable development goal at the faculty level to highlight inter-faculty collaborations
Bibliographic record
Abstract
Purpose Achieving the United Nations’ Sustainable Development Goals (SDGs) requires partnerships across nations, sectors and stakeholders. In academia, interdisciplinary research can help to address complex challenges related to the Goals. This paper aims to offer a structured approach to identifying current and potential research collaborations across faculties at a Canadian university. Design/methodology/approach Publications from the Dimensions database that had been assigned to an SDG category were matched against publications indexed by the university’s Research Information Management System (RIMS). The resulting matches were then sorted and tabulated by Australian and New Zealand Standard Research Classification research category and by the faculty affiliation of the authors. Potential interdisciplinary research collaborations are then identified by matching authors from different faculties who both have publications within the same research category. Findings Findings demonstrate that institutions can apply this methodology to track SDG-related publications, to analyse current interdisciplinary publications and to identify potential interdisciplinary collaborations. Since 2018, 95% of McMaster University’s SDG-related publications are authored by a researcher or researchers from a single faculty, and 5% are authored by researchers from two or more faculties. The interdisciplinary research collaborations were found to have a lower average citation impact and alternative metric scores than those publications with authors from a single faculty. Using a test case, 28 researchers from two faculties were identified as having common research interests with the potential to collaborate on a specific sustainability-related topic. Leveraging this methodology and an institution’s RIMS system provides university leaders with insight to track progress and plan research activities across the institution. Originality/value The analysis methods followed in this study highlight the importance of interdisciplinary research collaborations and may be valuable to institutions wanting to benchmark their own SDG efforts. Moreover, a simple methodology is presented for re-combining the data on prior collaborations to identify opportunities for new collaborations between faculties. This process combines the power of data processing with the user’s contextual insights to uncover novel pairings of faculty members whose research is aligned.
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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 | 0.016 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.083 | 0.133 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.012 | 0.004 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".