Toward Understanding Research Evolution on Indirect Drivers of Ecosystem Change along the Interface of Protected and Non-Protected Lands
Bibliographic record
Abstract
Against a backdrop of rapid environmental degradation and increasing pressures on natural resources, a broad list of innovations has emerged to support the vision of the post-2020 Kunming-Montreal Global Biodiversity Framework and strengthen regional and country-level biodiversity strategies along the interface of protected areas and non-protected lands. The success of these strategies depends in large part on science-informed consideration and approaches to the underlying and indirect drivers of change for natural systems and ecosystem services. This paper aims to inform future strategies and action plans for conservation efforts and sustainable practices globally and regionally, with a specific focus on Latin America’s environmental challenges. Bibliometric analysis, covering two decades from 2003 to 2023, focused on global and Latin American research trends related to the indirect drivers of change for natural systems and ecosystem services at the interface of protected and non-protected lands. Through structured analysis, key opportunities for increased collaboration, impact, and research focus are identified, highlighting the need to expand research collaboration strategies and reach, enhance research dissemination through open and equitable innovations, and strengthen capacity to the complex and interrelated challenges underlying accelerated change in natural systems, which affects biodiversity and ecosystem services.
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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 | 0.014 | 0.038 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.031 | 0.050 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.012 | 0.018 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".