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 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 | 0.026 | 0.042 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".