Understanding how governance emerges in social-ecological systems: insights from archetype analysis
Bibliographic record
Abstract
This paper is motivated by the question: how does governance emerge within social-ecological systems (SESs)? Addressing this question is critical for fostering sustainable transformations because it directs attention to the context specific and process intensive nature of governance as arising from the internal dynamics (i.e., interplay of feedbacks and interdependencies between the components) of SESs. This contrasts with the commonly held view of governance as an external intervention applied to a system. To systematically examine the recurrent patterns in how the internal dynamics promote/detract from the emergence of different types of governance, we applied archetype analysis to 60 selected cases of irrigation systems from Asia. Drawing inspiration from grid-group typology of cultural theory, we developed four specific archetypes: egalitarian, individualist, hierarchical, and fatalist. To build these archetypes, we applied a robustness framework and several other theories/perspectives to identify the different social-ecological and infrastructural attributes of irrigation SESs, and their interdependencies and feedback structures. We then used these attributes, identified through our theoretical review, to deductively code our selected cases and classify them into the different archetypes. The results show the different configurations of attributes that co-occur in each archetype, and how together these attributes and their inter-relationships lead to specific types of governance. Our archetype analysis also provides several interesting examples of fine-tuning between different SES attributes and how this fine-tuning is being threatened by various social and environmental changes. Through a systematic exploration of recurrent patterns using archetype analysis, our work builds on past efforts to apply ideas from complexity theory—specifically emergence—to unpack the complexities of SESs and offer practical guidance for fostering sustainability.
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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 | 0.004 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.002 | 0.010 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".