The evolution of social-ecological systems (SES) research: a co-authorship and co-citation network analysis
Bibliographic record
Abstract
Social-ecological systems (SES) research has gained substantial momentum, as witnessed by the growth in SES publications, theories, and frameworks, and the traction these concepts have gained in development and policy arenas. However, the growth and development of the SES field has only been partially examined, which limits our ability to make sense of and support the future development of the field and its ability to inform pressing sustainability challenges. The aim of this study is to understand how SES research has grown and changed over time as a field of study using bibliometric methods, co-authorship and co-citation network analysis. Our study is informed by broader bodies of work that have sought to understand the development of scientific fields, concepts, and research agendas. We highlight key trends that have influenced the organization of the field as well as how key thematic areas of SES research have evolved over time. Our results indicate that the research on SES is (i) mainly carried out by authors located in North America and Europe, (ii) characterized by changes in the terminology employed, as identified through our search terms, (iii) linked to the emergence of major conferences and centers dedicated to SES research, as well as its growth over time, (iv) characterized by a highly interconnected structure, with almost 80% of scholars being connected to each other, and (v) characterized by a shift in citation patterns, with newcomers in the network carving out their niche and replacing the founding figures as the central focus. We discuss the implications of these findings, including the nature of SES research as an “epistemic network,” the highly collaborative nature of SES research, and the role played by open-access journals in the growth of SES research in the digital era. We further suggest that the SES research field is at a critical transition point, with contending visions of its future following a more disciplinary path or remaining as a more open interdisciplinary space. We conclude with the questions this raises for future SES research regarding the implications of this duality on the nature, production, and validation of knowledge and its evolution.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.071 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.081 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".