Integrating Insights from Social-Ecological Interactions into Sustainable Land Use Change Scenarios for Small Islands in the Western Indian Ocean
Bibliographic record
Abstract
Small islands are vulnerable to the synergistic effects of climate change and anthropogenic disturbances due to the fact of their small area, geographical isolation, responsive ecologies, rapidly growing and developing populations and exposure to sea level and climate change. These changes exert pressures on ecosystem services, such as the provisioning of resources, and therefore threaten the sustainability of livelihoods. We reviewed key sustainability and livelihoods literature to bring together concepts of environmental livelihood resilience and stability across temporal and spatial scales and integrated them to produce a new conceptual framework for dynamic environmental livelihood sustainability (DESL). This framework aims to facilitate the incorporation of local community perspectives into water, energy and food nexus thinking about sustainable land use to support local livelihoods. Finally, we provide insights from this case study to evaluate the effectiveness of the DESL framework in addressing gaps in existing frameworks. We suggest this framing provides a mechanism for enhancing the agency of communities to produce more cohesive and inclusive land use management plans that can lead to enhanced environmental sustainability pathways.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".