Evaluation of OECD Countries with Multi-Criteria Decision-Making Methods in terms of Economic, Social and Environmental Aspects
Bibliographic record
Abstract
Exhausted natural resources and deteriorating ecological balance, together with the social privileges that people expect to have, are proof that the development of countries cannot be reduced to economic development alone. In this respect, this study aimed to evaluate the economic, social and environmental aspects of Organization for Economic Co-operation and Development (OECD) countries. Within this scope, the countries were firstly divided into two groups by performing cluster analysis in order to create more homogeneous country groups. Then, 12 criteria, consisting of four economic, four social and four environmental criteria, were determined by considering the literature and expert opinions. The criteria importance through intercriteria correlation (CRITIC) method was used to weight the determined criteria and using the calculated criterion weights, the countries in each cluster were then evaluated with the measurement of alternatives and ranking according to compromise solution (MARCOS) method. As a result, the most successful countries in the first cluster were determined as Switzerland, Denmark and Ireland with 68.8%, 62.7% and 62.5% performance scores, respectively. Whereas, the most unsuccessful countries were USA, Canada and Australia with 49.8%, 50.0% and 50.1% performance scores, respectively. The most successful countries in the second cluster were found as Slovenia, Spain and Portugal with 65.9%, 65.5% and 64.5% performance scores, while the most unsuccessful countries were Turkey, Chile and Colombia with 45.9%, 55.4% and 55.9% performance scores, respectively. Finally, in order to test the sensitivity of the MARCOS method, the solution was repeated with the MAIRCA, WASPAS, MABAC and CoCoSo methods using the weights obtained by the CRITIC method. A high correlation (greater than 80%) was found between the rankings acquired using the other methods and the rankings obtained by the MARCOS method.
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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 | 0.016 | 0.020 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".