Os países da União Europeia (UE) e a implementação da economia circular : uma análise comparativa
Bibliographic record
Abstract
In order to evaluate the performance of the European Union (EU) 27, its supranational regions and its Member States (MS), in the field of Circular Economy (CE) practices adopted, this research focused its efforts on a quantitative analysis, applying the Alkire-Foster (AF) method, through which the development of the Multidimentional Circular Economy Index (MCEI) came to fruition. Results show that, even though two different time periods are considered, 2008-15 and 2016-21, in both of them, the results stay almost the same, with a few exceptions scattered across the EU27. For both time periods, most MS perform poorly in the implementation of a circular economy when compared to the EU expectations. The Central and Eastern Europe and Northern Europe regions seem to be the regions with the least satisfactory performance regarding CE practices adopted. The reason lies in the low resource productivity present in the Estonia, Latvia and Lithuania MS, which has been partially compensated by the EU structural investment funds (Staehr & Urke, 2022, p.1053). A great disparity in MS individual composite scores, within the same region is also observed in all of the four supranational regions considered, meaning that the promotion of these practices comes, at first hand, from a national policy perspective.
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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 | 0.015 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.019 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".