More streamlined and targeted. A comparative analysis of the 7th and 8th Environment Action Programmes guiding European environmental policy
Bibliographic record
Abstract
Environment Action Programmes (EAP's) are the most important documents defining the environmental policies within the European Union. Their implementation, over the previous 50 years, represented a significant advance in raising eco-friendly awareness and suggesting solutions for environmental problems in the European Union. In this paper, we used Institutional Grammar Tool and network analysis to identify the evolution of EU EAP's by investigating the most recent two programmes (7th Environment Action Programme and 8th Environment Action Programme), particularly in priority objectives, institutional statements, enforcement perspectives, and projected participation of stakeholders. We found that the EU's 8th Environment Action Programme (2021-2030) is further streamlined and target oriented as compared to 7th Environment Action Programme. Furthermore, institutional statements included in the 8th EAP will be implemented predominantly at the levels of European Union and European Commission. On the contrary, in the 7th EAP, the number of institutions, frameworks, and stakeholders is higher and often regional and local (e.g., European Union, Environment Action Programme, European Environment Agency, European Commission, European Parliament, Convention on Biological Diversity, regional authorities, local authorities). The close links of the 8th EAP targets with the 2030 Agenda for Sustainable Development and the European Green Deal represent an important step towards a greater applicability of environmental policies in the European Union. Our study reveals that comparative analysis of legal documents using Institutional Grammar Tool and network analysis can assist policymakers in assessing the drafting of legal environmental documents and obtain indispensable information about the changes to improve environmental policies.
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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 | 0.011 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.007 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| 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".