Thinking Like a Mountain: Exploring the Potential of Relational Approaches for Transformative Nature Conservation
Bibliographic record
Abstract
Building on a review of current mainstream paradigms of nature conservation, the essence of transformations necessary for effective and lasting change are presented—namely, convivial solutions (or ‘living with others’), in which relationality and an appreciation of our interdependencies are central, in contrast to life-diminishing models of individualism and materialism/secularism. We offer several areas for improvement centred on regenerative solutions, moving beyond conventional environmental protection or biophysical restoration and focusing instead on critical multidimensional relationships—amongst people and between people and the rest of nature. We focus, in particular, on the potential of people’s values and worldviews to inform morality (guiding principles and/or beliefs about right and wrong) and ethics (societal rules defining acceptable behaviour), which alone can nurture the just transformations needed for nature conservation and sustainability at all scales. Finally, we systematize the potential of regenerative solutions against a backdrop of relational approaches in sustainability sciences. In so doing, we contribute to current endeavours of the conservation community for more inclusive conservation, expanding beyond economic valuations of nature and protected areas to include more holistic models of governance that are premised on relationally-oriented value systems.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.005 | 0.041 |
| Scholarly communication | 0.011 | 0.014 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".