THE ECO-SOCIO-ECONOMIC LEVEL OF DEVELOPMENT OF WORLD COUNTRIES – A COMPREHENSIVE ASSESSMENT PROPOSAL
Bibliographic record
Abstract
The main purpose of the article was to assess the eco-socio-economic development of world countries. For this purpose, the Comprehensive Eco-Socio-Economic Development Index (CESEDI) was proposed and used. The proposed measure is based on a dozen or so indicators recognized and used in the literature for assessing countries in terms of their social, economic and environmental achievements. An attempt was made to include most of the elements necessary for the safe, healthy and happy life of citizens of the studied countries. The article presents world leaders, based on the CESEDI. Moreover, the individual components of the CESEDI and their level in the analyzed countries are presented. It was found, inter alia, that 18 out of 20 countries with the highest CESEDI are European countries. The ranking leaders were highly developed Scandinavian countries (Norway, Denmark, Finland) and Switzerland. The countries of Eastern and South-Eastern Europe (Slovenia, Slovakia, the Czech Republic, Poland and Romania) took high positions in the ranking, ahead of such countries as Canada, the United Kingdom, Japan and the United States. Research results indicate that European and South American countries are, on average, more developed in terms of ecological, social and economic development than countries in the rest of the world.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.016 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".