Indicators to assess viable entry points for implementing landscape approaches
Bibliographic record
Abstract
Integrated landscape approaches are gaining momentum, but there is a lack of evidence on how to get started. Bringing multiple stakeholders together to negotiate trade-offs between conservation and development, as well as competing land uses, is an ambitious goal involving high transaction costs. There is a need to identify entry or leverage points for implementing landscape approaches and assess their potential. Although principles and criteria for landscape approaches are available, few studies provide concrete indicators to assess the viability of such entry points. This paper addresses this gap. Drawing on a systematic literature review and expert insights, we propose a set of indicators aligned with principles related to foundational conditions, stakeholder engagement, orientation toward landscape outcomes, negotiation processes, and learning, monitoring, and evaluation. These indicators can be used to assess the potential and limitations of landscape initiatives to evolve into full landscape approaches.
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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 | 0.124 | 0.266 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.026 | 0.018 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.008 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".