Strategies of universities in northern Europe and North America in the field of sustainable development
Bibliographic record
Abstract
The article presents an analysis of the strategies of universities in Nordic countries and North America to achieve the Sustainable Development Goals. It demonstrates that the sustainable development of northern territories holds one of the first places on the scientific and technical agenda of the leading countries of world science. The article discusses the methods and practices used to implement the concept of sustainable development and the principles of equality and inclusivity. It also discusses measures taken to preserve the languages and cultures of the indigenous peoples in the North, as well as efforts to promote diversity and engagement. It is shown that a significant part of the long-term objectives defined in the strategies of universities in Northern Europe and North America is aimed at increasing their contribution to the sustainable development of regions, a climate-neutral society, reducing inequality and the development of indigenous peoples. Based on the case studies of universities in Finland, Sweden, Norway, Greenland, Canada and the USA, recommendations are given for Arctic universities in Russia on the development and adjustment of ESG strategies.
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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 | 0.010 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.010 | 0.005 |
| Scholarly communication | 0.010 | 0.003 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".