Studying human-nature relations in aquatic social-ecological systems using the social-ecological action situations framework: how to move from empirical data to conceptual models
Bibliographic record
Abstract
Various analytical frameworks have been developed to examine the interactions between humans and nature in social-ecological systems (SES). When studies of SES do not make explicit how a framework is used to build conceptual models, they leave a black box for scholars aiming to use such frameworks. Our study highlights such a process, focusing on the analytical step of transitioning from empirical data to categories of the recently developed social-ecological action situations (SE-AS) framework. This framework, which proposes to study SES along configurations of action situations (AS), has so far been applied in few studies. Clarifying how data are analyzed and adapted within this framework is thus of relevance for anyone interested in using it. We compare two analytical methods used to identify AS configurations, each method serving for the analysis of one of two lake-catchment areas, located in Northern Germany and Canada. The first method is a qualitative interview content analysis based on text coding; the second is the analysis of causal loop diagrams (CLD) derived from interviews. Both data sources outline stakeholder social representations of their SES. We compare: (1) the suitability of the two analytical methods for identifying configurations of AS and their linkages, and (2) the potential and limitations of the SE-AS framework for assessing the inherent dynamics of emergent phenomena within SES. Our two-pronged data analysis methodology led to similar results despite the different analytical methods used. We conclude that the SE-AS framework can help illustrate different interactions within an SES while allowing for relatively simple, yet comprehensible system representations. We also identify challenges and limitations that we encountered. Challenges revolve around identifying AS and differentiating them from outcomes. Limitations include the representation of time and levels of governance, and weighting the relative importance of AS.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".