Mobilizing Global Change Science for Effective Multi-Actor Governance in the Laguna San Rafael and Guayaneco Biosphere Reserve
Bibliographic record
Abstract
The 1950s initiated transformative shifts in human interactions and societal behaviors, exacerbating global environmental challenges—notably, biodiversity loss. The post-2020 Kunming–Montreal Global Biodiversity Framework (GBF) addressed these challenges with ambitious plans to halt and reverse biodiversity losses. Supported by initiatives like UNESCO’s Man and the Biosphere program, the GBF seeks to enhance sustainability through country-level strategies that will mainstream nature-positive policies and expand multi-actor conservation governance. This study supports the local-level implementation of the GBF through a roadmap for the initial phase of the knowledge-action network creation. Through a case study of the Laguna San Rafael and Guayaneco Biosphere Reserve (LSRGBR) in Chilean Patagonia, this research explores the potential for inexpensive, readily available methods to support local decision makers by increasing access to and the visibility of relevant sustainability research. The study analyzes two decades of global change (GC) research within LSRGBR zones to understand spatial trends and identify applied insights with the potential to inform governance and management strategies. Findings highlight where GC research has occurred, areas of GC research interest, how applied content has manifested, and how existing research can inform and support governance action plans. Ultimately, this research proposes an adaptable knowledge mobilization framework for the LSRGBR that can be applied to a variety of place-based needs and contexts to mobilize science for broader sustainability objectives and enhance the potential for multi-actor collaboration and governance.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.001 | 0.001 |
| 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".