Bio-indicators as a Measure of Social Fragility
Bibliographic record
Abstract
Disaster risk results from the interaction between hazards and vulnerabilities, but there are considerable variations in how vulnerability and its three dimensions (exposure, fragility, and resilience) are conceptualized and measured. This study demonstrates how certain bio-indicators allow an objective, direct, and efficient measurement of a population's social fragility. Using data available for 159 countries, we selected two bio-indicators, Low Birth Weight (LBW) and Life Expectancy at Birth (LEB), and developed the Social Fragility Index (SFI). We then analysed their effect on existing vulnerability indices: the Susceptibility Index (SI) and the Prevalent Vulnerability Index (PVI). Results showed that the selected bio-indicators and particularly the proposed index are efficient in measuring the fragility of a community before a disaster, and that they could also be used to measure the social impact caused by an extreme natural event, technological disasters, population displacement/migration, armed unrest, conflict, changes in political regimes, and economic crises.
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".