Who is researching biodiversity hotspots in Eastern Europe? A case study on grasslands from Romania
Bibliographic record
Abstract
Abstract Farming landscapes of Europe are vital arenas for social-ecological sustainability because of their significant coverage and potential to integrate food production with biodiversity conservation. Knowledge gathered by scientific research is a critical ingredient for developing and implementing socio-economically and ecologically sustainable grassland management strategies for grasslands. The quality of scientific knowledge and its potential to address grasslands as complex social-ecological systems is strongly dependent on the creativity and scientific ambition of the researcher, but also on the network (from academic and non-academic sectors) around the researcher. The goal of this paper is to map the research network around Romania’s grasslands. These systems have exceptional socio-cultural and economic values and are between the most biodiverse ecosystems of the world. Considering the multiple threats to these grasslands, it is an urgent need to understand the existing scientific knowledge profile around these systems. This paper aims at using bibliometrics analysis, a well-developed scientific domain that envisages network theory to analyze relationships between affiliations network, co-authorship network, and co-word analysis. The number of studies targeting grassland management in Romania is increasing mainly thanks to international involvement. However, the management of the grasslands is still deficient and the contribution of science to the process is virtually absent. The subject of research is mainly related to the biological and ecological characteristics of grasslands, a notable absence from internationally visible research being the management of grasslands, especially in the context of EU Common Agricultural Policies. To increase scientific performance, and better inform EU and local policies on grassland management, Romanian researchers should better capitalize on international collaborations and local academic leaders. Our findings can be used to identify research gaps and to improve collaboration and knowledge exchange between practitioners, scientists, policy makers, and stakeholders.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".