Synthesizing Archetypes of Social‐Ecological Systems: Identifying Common Building Blocks
Bibliographic record
Abstract
ABSTRACT A growing number of studies apply the social‐ecological systems (SES) framework with its standardized set of variables to examine place‐based environmental governance. Yet, due to the wide diversity of social‐ecological systems, a general theory about how variables interact—and systems can be governed—lacks empirical support. Despite many case studies, knowledge cumulation is hindered by data heterogeneity, and by the difficulties with synthesizing a large number of cases into middle‐range theories, possibly understood as re‐occurring patterns of the larger theoretical puzzle of environmental governance. Thus, this paper aims to cumulate knowledge by identifying repeating configurations of variables across 71 models from SES framework case studies using archetype analysis. We propose a building‐blocks approach to identify eight archetypes, each characterized by a triad (presence of three variables), an explanation of this triad, and a qualitative characterization with cases which exemplify them. The triads relate to, for example: shared operational agency; small households in remote, inaccessible places; property and accountability; or formal investment conditions. We show how a relatively small set of triads can be combined in various ways to represent a larger diversity of SES, and illustrate this by re‐visiting several cases. We argue that identifying these recurring archetypes advances the field because it allows scholars to focus their theorizing and empirical research around a known set of triads. More broadly, the paper contributes to advancing empirically supported claims about SES and environmental governance, new uses of the SES framework, and techniques for knowledge cumulation using archetype analysis.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 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".