Welcome home! Introducing SocSES: a society for inclusive and impactful social-ecological research
Bibliographic record
Abstract
Underpinned by systemic thinking, social-ecological systems (SES) research has emerged as a critical field for addressing the challenges of the Anthropocene, marked by a cross-scale focus, inter- and transdisciplinary approaches, and a strong emphasis on place-based work. Thanks to the efforts of many networks and institutes, the field has advanced new theoretical and methodological approaches, fostered dedicated journals, and spurred educational programs. It has also significantly influenced sustainability initiatives and policy from local to global scales, and has richly informed place-based efforts. Despite this progress, SES research faces persistent challenges, including conceptual and methodological fragmentation, difficulty in scaling localized insights to global frameworks (and vice versa), and capturing cross-scale connections and processes while retaining contextual relevance. Inclusivity also remains a critical issue, with regional, Indigenous, and local contributions often underrepresented, as there is still a reliance on short-term, inequitably distributed grant funding for much of the research in the field. This paper introduces the Society for Social-Ecological Systems (SocSES), a global platform designed to build on and connect to the rich legacy of SES networks. SocSES aims to advance and support SES–based research, practice, and action toward a just and sustainable future. We outline how SocSES will provide a home for SES institutes, networks, researchers, and practitioners working at the science-practice-policy interface to connect and amplify existing efforts through thematic streams, regional hubs, an institutional hub, an early-career professionals hub, and synthesis groups. The society will provide a stable infrastructure to foster interdisciplinary and transdisciplinary collaboration, enhance the generalizability and policy relevance of SES research, bolster education, research, and knowledge co-production, and support the next generation of SES professionals. By addressing the persistent challenges facing the field and fostering transformative spaces and communities for innovation and action, SocSES aspires to support and leverage SES knowledge as a cornerstone of global sustainability science. In line with the society’s commitment to linguistic diversity and equitable access, this abstract has been translated into 12 languages by authors of this paper and additional contributors. These translations are available in Appendix 2 and at https://socses.org/about/paper.
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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.007 | 0.013 |
| Scholarly communication | 0.024 | 0.028 |
| Open science | 0.003 | 0.034 |
| Research integrity | 0.010 | 0.014 |
| Insufficient payload (model declined to judge) | 0.090 | 0.036 |
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".