Disentangling the complexity of human–nature interactions
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
Abstract Human–nature interactions have been identified as an important leverage point for achieving sustainability. Processes to recognize, protect, improve and reimagine human–nature interactions will be central to shift the world to more sustainable and equitable pathways and futures. In the context of the interconnected and rapidly changing Anthropocene, work on human–nature interactions must move beyond dominant linear assumptions of a relatively simple and easily and predictably manipulated world to acknowledge and engage with the complex, dynamic, asymmetrical and unequal nature of the interactions connecting people and nature. Based on three key features highlighted by the study of complex social–ecological systems (SES)—that these systems are relational, open and dynamic—we propose three new directions for the study and management of human–nature interactions that can help to acknowledge and disentangle the globally intertwined and dynamic nature of these interactions. These features suggest new directions and foci for sustainability science: the inseparable and relational qualities of the interactions between people and nature; the cross‐scale nature of these relationships; and the continuously evolving and changing form of these relationships. To bridge the gap between the theory of complex, inseparable and unequal human–nature interactions and the reductionist tendencies in research and practice, SES research raises opportunities to connect local action and global learning; to mobilize and develop new cross‐scale and relational capacities to encourage synergies and avoid trade‐offs; and to explore, experiment and learn our way forward onto more sustainable and equitable pathways. Read the free Plain Language Summary for this article on the Journal blog.
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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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