Achieving the Promise of Integration in Social-Ecological Research: A Review and Prospectus
Bibliographic record
Abstract
An integrated understanding of both social and ecological aspects of environmental issues is essential to address pressing sustainability challenges. An integrated social-ecological systems perspective is purported to provide a better understanding of the complex relationships between humans and nature. Despite a threefold increase in the amount of social-ecological research published between 2010 and 2015, it is unclear whether these approaches have been truly integrative. We conducted a systematic literature review to investigate the conceptual, methodological, disciplinary, and functional aspects of social-ecological integration. In general, we found that overall integration is still lacking in social-ecological research. Some social variables deemed important for addressing sustainability challenges are underrepresented in social-ecological studies, e.g., culture, politics, and power. Disciplines such as ecology, urban studies, and geography are better integrated than others, e.g., sociology, biology, and public administration. In addition to ecology and urban studies, biodiversity conservation plays a key brokerage role in integrating other disciplines into social-ecological research. Studies founded on systems theory have the highest rates of integration. Highly integrative studies combine different types of tools, involve stakeholders at appropriate stages, and tend to deliver practical recommendations. Better social-ecological integration must underpin sustainability science. To achieve this potential, future social-ecological research will require greater attention to the following: the interdisciplinary composition of project teams, strategic stakeholder involvement, application of multiple tools, incorporation of both social and ecological variables, consideration of bidirectional relationships between variables, and identification of implications and articulation of clear policy recommendations.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".