Assessing Countries Sustainability: A Group Multicriteria Decision Making Methodology Approach
Bibliographic record
Abstract
Sustainability is a complex and abstract concept. However, policy-makers and representatives of global and regional associations need to assess and track the sustainable development of countries and regions to define a sustainability strategic path. The objective of this research is to propose and validate a methodology to define a simple but proper sustainability index that serves as a proxy for the identification of the segments of most and least advanced countries according to their achievement of the sustainable development goals defined by the United Nations (UN). Several well-known quantitative methodologies are used to first define a summarized index of sustainable development. Second, multicriteria decision-making methods are applied to determine the relative importance of the elements or dimensions comprising the sustainability concept. Then, the simulated judgments of a group of experts is used to compute a group weight vector by applying the Fuzzy Analytic Hierarchy Process (FAPH). Different aggregation methods are used to compute the importance that decision-makers assign to the several dimensions of sustainability. Finally, segments of countries generated with the clustering algorithm k-means are rated to identify sustainability benchmark segment(s) and groups of countries in need of support to attain the UN sustainability goals.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".