Operationalizing the integrated landscape approach in practice
Bibliographic record
Abstract
The terms "landscape" and "landscape approach" have been increasingly applied within the international environmental realm, with many international organizations and nongovernmental organizations using landscapes as an area of focus for addressing multiple objectives, usually related to both environmental and social goals.However, despite a wealth of literature on landscapes and landscape approaches, ideas relating to landscape approaches are diverse and often vague, resulting in ambiguous use of the terms.Our aim, therefore, was to examine some of the main characteristics of different landscape approaches, focusing on how these might be applied in the process of taking a landscape approach.Drawing on a review of the literature, we identify and discuss three different kinds of landscape approaches: using the landscape scale, a sectoral landscape approach, and an integrated landscape approach.Focusing on an integrated landscape approach, we examine five concepts to help characterize landscape approaches: multifunctionality, transdisciplinarity, participation, complexity, and sustainability.For each term, a continuum of application exists.To help improve and move the integrated landscape approach more toward operationalization, more focus needs to be placed on the process of taking the approach.Although the process can be implemented in a range of ways, in a more integrated approach it will require explicitly defined objectives as well as a clear understanding of what is meant by multifunctionality and sustainability.It will also require collaborative participation, transdisciplinarity/cross-sectoral approaches, managing for adaptive capacity, and applying an iterative process to address the inherent complexity within the system.Although these concepts are not new, we present continuums on which they can exist, allowing for clarification and distinctions to be made regarding what it means to take a landscape approach.
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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 | 0.029 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.004 | 0.036 |
| Scholarly communication | 0.018 | 0.022 |
| Open science | 0.003 | 0.015 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".