Proposals of European Citizens for Reviving the Future of Shrinking Areas
Bibliographic record
Abstract
Abstract Shrinkage, depopulation and the related structural decline threaten development trajectories of more than a quarter of European territories from the present until 2050. In April 2021, the European Commission has launched the Conference on the Future of Europe to involve citizens and players beyond the traditional actors in shaping future policy agendas. The initiative consists of a wide-scale citizen engagement policy offering them a digital framework to actively contribute to the most relevant debates from April to December 2021. Given that shrinkage is a neglected theme in traditional policy arenas, this article examines the proposals of European citizens for reviving the future of shrinking areas. Through content analysis, the article highlights a limited relative presence of shrinkage in the Conference debate. Nevertheless, the results offer insights into the thematic concentration and the affinity of shrinkage with the most popular policy debates. The article also discusses the content of citizens’ ideas for the future of shrinking areas, thus offering concrete proposals that may fuel the definition of future policy agendas.
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 | 0.037 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.002 | 0.012 |
| Research integrity | 0.010 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 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".