Bibliographic record
Abstract
Small island states and jurisdictions face enormous sustainability challenges such as isolation from global markets, tenuous resource availability, heavy reliance on imports to meet basic needs, 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 island economies’ progress towards sustainability, and consistently rank them high on various vulnerability indices. This talk introduces the concept of socio-metabolic risk, 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. Drawing on years of socio-metabolic research on islands, I will argue that specific configurations and combinations of material stocks and flows and their ‘resistance to change’ contribute to the system’s proliferation of socio-metabolic risk (SMR). For better or for worse, these influence the system’s ability to consistently and effectively deliver societal services necessary for human survival. Governing SMR would mean governing socio-metabolic flows, and easing resource requirements through green(-blue) infrastructure and nature-based solutions (NBS) to provide crucial societal services. Such interventions will need strategies to reconfigure resource-use patterns and associated services that are sustainable as well as 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.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".