Sustainability in the European Union: Analyzing the Discourse of the European Green Deal
Bibliographic record
Abstract
In the European Union, the concern for sustainability has been legitimized by its politically and ecologically motivated discourse disseminated through recent policies of the European Commission and the local as well as international media. In the article, we question the very meaning of sustainability and examine the European Green Deal, the major political document issued by the EC in 2019. The main question pursued in the study is whether expectations verbalized in the Green Deal’s plans, programs, strategies, and developments hold up to the scrutiny of critical discourse analysis. We compare the Green Deal’s treatment of sustainability to how sustainability is presented in environmental and social science scholarship and point out that research, on the one hand, and the politically motivated discourse, on the other, do not correlate and often actually contradict each other. We conclude that sustainability discourse and its keywords, lexicon, and phraseology have become a channel through which political institutions in the EU such as the European Commission sideline crucial environmental issues and endorse their own presence. The Green Deal discourse shapes political and institutional power of the Commission and the EU.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.018 | 0.024 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.008 | 0.024 |
| Scholarly communication | 0.018 | 0.015 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.006 | 0.004 |
| 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".