SDGs: A Responsible Research Assessment Tool toward Impactful Business Research
Bibliographic record
Abstract
An alternative research assessment (RA) tool was constructed to assess the relatedness of published business school research to the United Nations’ 17 Sustainable Development Goals (SDGs). The RA tool was created using Leximancer™, an on-line cloud-based text analytic software tool, that identified core themes within the SDG framework. Eight (8) core themes were found to define the ‘spirit of the SDGs’: Sustainable Development, Governance, Vulnerable Populations, Water, Gross Domestic Product (GDP), Food Security, Restoration, and Public Health. These themes were compared to the core themes found in the content of 4576 academic articles published in 2019 in journals that comprise the Financial Times (FT) 50 list. The articles’ relatedness to the SDG themes were assessed. Overall, 10.6% of the themes found in the FT50 journal articles had an explicit relationship to the SDG themes while 24.5% were implied. Themes generated from machine learning (ML), augmented by researcher judgement (to account for synonyms, similar concepts, and discipline specific examples), improved the robustness of the relationships found between the SDG framework and the published articles. Although there are compelling reasons for business schools to focus research on advancing the SDGs, this study and others highlight that there is much opportunity for improvement. Recommendations are made to better align academic research with the SDGs, influencing how business school faculty and their schools prioritize research and its role in the world.
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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 | 0.333 | 0.516 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.005 |
| Bibliometrics | 0.064 | 0.038 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.019 | 0.020 |
| Open science | 0.004 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.014 | 0.006 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".