Regenerative landscape design: an integrative framework to enhance sustainability planning
Bibliographic record
Abstract
Addressing contemporary environmental and social crises requires solutions-based, systems-level changes. To achieve these changes, transdisciplinary research approaches are needed to align problem framing with solution deployment at landscape scales. However, practical frameworks to guide this work are lacking. Here we propose a new framework to help bridge this gap: regenerative landscape design (RLD). We define RLD as a process for finding pattern-based solutions, emphasizing cooperative, iterative, and facilitated engagement for the co-production of locally relevant knowledge for desirable landscape stewardship. To do so, we review how key components of RLD (e.g., landscapes, design thinking, and regenerative processes) have been differentially and unevenly applied in disciplines ranging from resilience, landscape ecology, geography, architecture, agriculture, sociology, tourism, and more. We then put forward research considerations of a RLD approach to enhance social and environmental well-being. We use two emerging case studies (i.e., Chesapeake Bay Watershed, Pennsylvania, USA and Narok County, Kenya) to put forward pathways for implementation of the RLD strategy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.004 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.016 | 0.012 |
| Open science | 0.005 | 0.012 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 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".