Areas of social impact of top-listed universities worldwide
Bibliographic record
Abstract
This chapter aims to introduce the key areas of universities’ social impact out of the Times Higher Education Impact Rankings (THE Impact Ranking). To reach the aim, we adopted detailed research questions: (1) Is there a leading university that has maintained a high position since it first appeared in the ranking? (2) In which countries are the top 10 universities located? (3) Which goals are least often indicated by the top 10 universities? (4) In which Sustainable Development Goals (SDGs) did the top 10 universities achieve the highest score? We analyze the top 10 universities, starting from 2020. In the ranking, the universities’ impact is assessed by its achievements in SDGs. The chapter is analytical. The authors carried out a critical analysis of the literature on the subject and presented the findings, which indicate a steady increase in the number of universities from different regions of the world that are pursuing selected SDGs in their activities. The top 10 list in the THE Impact Ranking is dominated by universities from Australia and Canada. Certain goals – no. 2 (zero hunger), no. 1 (no poverty), no. 14 (life below water), and no. 7 (affordable and clean energy) were least often indicated by the top 10 universities.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".