Rewilding relationships: Principles for forging relationships in social-ecological systems
Bibliographic record
Abstract
Rewilding deliberately forges new relationships within complex socio-ecological systems. Yet, many rewilding initiatives proceed without fully considering the multitude of relationships at play. In this paper, we advance a framework that reimagines rewilding as a relationship-centered process, emphasizing that success depends on fostering connections from individual to collective levels for humans and non-humans alike. To illustrate this, we focus on species (re)introductions, identifying the various collective and individual relationships that shape rewilding outcomes. We then propose five principles for effectively forging these relationships: (1) reconsider values and perceptions of nature; (2) embrace a collective and individual-oriented approach; (3) place local communities at the heart of rewilding initiatives; (4) cautiously revive lost relationships; and (5) strengthen the connection between science and policy. Our framework demonstrates that identifying and fostering these relationships is not just essential but transformative, paving the way for rewilding practitioners to create ethical, interconnected, and resilient socio-ecological systems.
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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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".