Socio-metabolic risk and tipping points on islands
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.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".