Using a leverage points perspective to compare social-ecological systems: a case study on rural landscapes
Bibliographic record
Abstract
A leverage points perspective recognises different levels of systemic depth, ranging from the relatively shallow levels of parameters and feedbacks to the deeper levels of system design and intent. Analysing a given social-ecological system for its characteristics across these four levels of systemic depth provides a useful diagnostic to better understand sustainability problems, and can complement other types of cause-and-effect systems modelling. Moreover, the structured comparison of multiple systems can highlight whether sustainability challenges in different systems have a similar origin (e.g. similar feedbacks or similar design). We used a leverage points perspective to systematically compare findings from three in-depth social-ecological case studies, which investigated rural landscapes in southeastern Australia, central Romania, and southwestern Ethiopia. Inductive coding of key findings documented in over 60 empirical publications was used to generate synthesis statements of key findings in the three case studies. Despite major socioeconomic and ecological differences, many synthesis statements applied to all three case studies. Major sustainability problems occurred at the design and intent levels. For example, at the intent level, all three rural landscapes were driven by goals and paradigms that mirrored a productivist green revolution discourse. Our paper thus highlights that there are underlying challenges for rural sustainability across the world, which appear to apply similarly across strongly contrasting socioeconomic contexts. Sustainability interventions should be mindful of such deep similarities in system characteristics. We conclude that a leverage points perspective could be used to compare many other types of social-ecological systems around the world.
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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 | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.011 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".