Organizational transformation for greater sustainability impact: recent changes in a scientific research infrastructure in Europe
Bibliographic record
Abstract
Context: Scholars across holistic, transdisciplinary, place-based fields of research, such as landscape ecology and social ecology, have increasingly called for an 'all-hands-on-deck' approach for transformations toward greater sustainability of social-ecological systems. This Perspective showcases organizational transformation toward sustainability in the context of a research network dedicated to place-based, social-ecological research in Europe. Objectives: Using the European LTER research infrastructure (eLTER RI) as a case, we analyze recent organizational-level shifts motivated by desires to increase sustainability impact. These shifts include knowledge integration between the natural and social sciences, stakeholder engagement, and a reformulation of administrative guidelines and practices. Methods: Following a program evaluation, new conversations led to new initiatives in the eLTER RI. As researchers who were involved in the program evaluation and the development of new initiatives, we rely on our professional experience and participant observation to provide insights about this process and its developments. Results: Recommendations from a recent assessment that critiqued and provided recommendations for the research infrastructure have recently been implemented in the eLTER RI. eLTER has leveraged a unique and timely opportunity-formal recognition and project funding by the EU-to upscale and standardize its infrastructure by creating novel protocols and enacting steps towards implementation. Conclusions: This Perspective demonstrates how eLTER's research agenda and related protocols have evolved to better integrate multiple knowledge types, promote stakeholder integration into research, and foster greater equity and reflexivity in doing science, all of which are considered necessary to increase sustainability impact. We conclude by considering current and potential future challenges.
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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 | 0.104 | 0.077 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.007 | 0.019 |
| Scholarly communication | 0.025 | 0.015 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.008 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 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".