Sustainability policy considerations for ecosystem management in central and eastern europe
Bibliographic record
Abstract
Abstract Here I discuss Central and Eastern European (CEE) countries as a region undergoing rapid change, resulting from the collapse of the Soviet Union and admission of some of the states into the European Union. These events brought changes in governance and ecosystem management, triggering impacts on land use and biodiversity. What are some of the policy options toward sustainability in the face of these political, governance, and socioeconomic changes? Some policy considerations for ecosystem management and sustainability include taking a social–ecological systems approach to integrate biophysical subsystems and social subsystems; paying attention to institutions relevant to shared resources (commons) management; and using resilience theory to study change and guidance for governance. Documented experience in CEE seems to indicate shortcomings for both the centralized state management option and the purely market‐driven option for ecosystem management. If so, a “smart mix” of state regulations, market incentives, and self‐governance using local commons institutions may be the most promising policy option to foster ecosystem stewardship at multiple levels from local to international.
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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.005 | 0.003 |
| 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".