Network Theory in the Assessment of the Sustainability of Social–Ecological Systems
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 As human activities increasingly threaten the ecosystems on which they depend, one of the main questions our societies are facing is related to the resilience – seen as a necessary element of sustainability – of social–ecological systems (SESs). SESs are composed of many heterogeneous elements including human actors such as institutions and resource users, and natural components such as land patches, animal species, etc. The numerous relationships between these different entities shape complex, dynamic networks of social–ecological interdependencies. Once described as networks, SESs can be analysed using a variety of network metrics, which may potentially help to better quantify and evaluate the resilience of SESs to external or internal perturbations. In this paper, we provide a broad overview of the latest progress in network theory as applied to SESs and discuss how network metrics may be used to assess the sustainability of an SES.
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 | 0.003 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it