Bibliographic record
Abstract
Green growth mitigates greenhouse gas emissions and prevents environmental degradation.It creates new growth engines and jobs.A green economy is characterised as a public good.In it, income growth and employment should be driven by public and private investment (UNEP, 2011).The main purpose of this paper is to present a general picture of green growth for the Balkan countries that are not part of the European Union, as well as to evaluate the indicators where these economies have performed better and where they need to intervene in order to improve.To achieve this goal, the paper uses data obtained from OECD.Stat.The OECD Green Growth data source contains specific indicators to monitor improvement through green growth.We selected data for five Balkan countries that are not part of the European Union (Albania, Serbia, Montenegro, Bosnia and Herzegovina, North Macedonia) for the years from 1990 to 2020.The variables used in this paper are indicators of green growth, and we will use them to observe which of the countries taken in the study has progressed more towards green growth.The results of the paper guide governments to design relevant policies in those variables where they have performed the weakest.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".