Socio-metabolic risk and tipping points on islands
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 Small Island Developing States (SIDS) face enormous sustainability challenges such as heavy reliance on imports to meet basic needs, tenuous resource availability, coastal squeeze, and reduced waste absorption capacity. At the same time, the adverse effects of global environmental change such as global warming, extreme events, and outbreaks of pandemics significantly hinder SIDS’ progress towards sustainable development. This paper makes a conceptual contribution by framing the vulnerability of small islands from the perspective of socio-metabolic risk (SMR). SMR is defined as systemic risk associated with the availability of critical resources, the integrity of material circulation, and the (in)equitable distribution of derived products and societal services in a socio-ecological system. We argue that specific configurations and combinations of material stocks and flows on islands and their ‘resistance to change’ contribute to the system’s proliferation of SMR. For better or for worse, these influence the system’s ability to consistently and effectively deliver societal services necessary for survival. By positioning SMR as a subset of systemic risk, the paper illustrates SMRs and tipping points on small islands using insights from three sectors: water, waste, and infrastructure. We also identify effective leverage points and adaptation strategies for building system resilience on small islands. In conclusion, our synthesis suggests that governing SMR on SIDS would mean governing socio-metabolic flows to avoid potential disruptions in the circulation of critical resources and the maintenance of vital infrastructures and services while inducing interventions towards positive social tipping dynamics. Such interventions will need strategies to reconfigure resource-use patterns and associated services that are sustainable and socially equitable.
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 | 0.002 | 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 | 0.002 | 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