Assessment of the Sustainability of Countries at Worldwide
Bibliographic record
Abstract
For the quantification and ranking of sustainablility reliable indicators are needed in the economic, social and environmental areas. For this, decision-making methods have been used to identify and rank the most important indicators. However, it is important to know which method to use, since this choice can modify the result. Therefore, two methods of multi-criteria decision making were evaluated: Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) and TOPSIS with Hierarchical Analytical Process (AHP). It was observed a difference between the methods tested, where the TOPSIS-AHP method presented better performance as a function of the weights assigned by the specialists. The research results demonstrated which countries have a more balanced sustainable development in environmental, social and economic levels together. In this case, the three most sustainable countries are Switzerland, Sweden and Norway. Additionally this research shows which countries are more sustainable taking into account each indicator separately. It is expected that the results provide a basis in decision-making and it contribute to the best choices in all aspects of sustainability.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".