Opportunities for and barriers to anticipatory governance of two lake social–ecological systems in Germany and Canada
Bibliographic record
Abstract
Abstract Climate change effects are already being felt around the globe, and governance systems need to adapt to this new reality to foster greater resilience in social–ecological systems (SES). Anticipatory governance is a concept proposed for such a purpose. However, its definition remains rather vague in the literature, as is its practical use for decision makers. In this paper, we contribute to filling these two shortcomings. First, we conducted a systematic literature review of the concept and derived the following main criteria: foresight, networked engagement, integration and feedback. Second, we use the identified criteria to analyse two social–ecological systems around lakes in Lower Saxony, Germany and in Quebec, Canada. In both cases, data were generated using a participatory approach (interviews and workshops) with local stakeholders. We examined these data, identifying opportunities and barriers to anticipatory governance. Our findings support, with empirical data for the first time, the claim in the literature that ensemble‐ization—the fact that all anticipatory governance criteria must be put forward jointly and not in isolation—is a facilitator for the emergence of anticipation. Furthermore, by highlighting opportunities and barriers to anticipatory governance within two temperate lake SES cases, we illustrate how to understand a given system's limitations with respect to anticipatory governance, as well as how to engage with the concept through concrete, already existing opportunities. The proposed course of actions could help design more anticipatory governance systems to support decision‐making processes that could enhance SES resilience. Read the free Plain Language Summary for this article on the Journal blog.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".