Landscapes—a lens for assessing sustainability
Bibliographic record
Abstract
Context: There are urgent calls to transition society to more sustainable trajectories, at scales ranging from local to global. Landscape sustainability (LS), or the capacity for landscapes to provide equitable access to ecosystem services essential for human wellbeing for both current and future generations, provides an operational approach to monitor these transitions. However, the complexity of landscapes complicates how and what to consider when assessing LS. Objectives: To identify important features of landscapes that remain challenging to consider in LS assessments and provide guidance to strengthen future assessments. Methods: We conducted two workshops to identify the complex features of landscapes that remain under-considered in LS assessments, and developed guidelines on how to better incorporate these features. Results: We identify open and connected boundaries and diversity of values as landscape features that must be better considered in LS assessments or risk exacerbating offstage sustainability burdens and power inequalities. We provide guidelines to avoid these pitfalls which emphasize assessing ecosystem service interactions across interconnected landscapes and incorporating local actors' diverse values. Conclusions: Our guidelines provide a stepping stone for researchers and practitioners to better incorporate landscape complexities into LS assessments to inform landscape-level decisions and actions.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.013 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".