Reconnecting with Nature through Good Governance: Inclusive Policy across Scales
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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 it