Reconnecting with Nature through Good Governance: Inclusive Policy across Scales
Bibliographic record
Abstract
We are disconnected from nature, surpassing planetary boundaries at a time when our climate and social crises converge. Even prior to the emergence of COVID-19, the United Nations and its member states were already off track to achieve the Sustainable Development Goals (SDGs) and fulfil climate commitments made under the Paris Agreement. While agricultural expansion and intensification have supported increases in food production, this model has also fostered an unsustainable industry of overproduction, waste, and the consumption of larger quantities of carbon-intensive and ultra-processed foods. By addressing the tension that exists between our current food system and all that is exploited by it, different scales of governance can serve as spaces of transformation towards more equitable, sustainable outcomes. This review looks at how good governance can reconnect people with nature through inclusive structures across scales. Using four examples that focus on place-based and rights-based approaches—such as inclusive multilateralism, agroecology, and co-governance—the author hopes to highlight the ways that policy processes are already supporting healthy communities and resilient ecosystems.
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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 | 0.014 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.038 |
| Scholarly communication | 0.013 | 0.016 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.005 | 0.005 |
| 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".