Unveiling global humanscapes: harmonised subnational socio-economic datasets for understanding societal changes and enhancing risk assessments
Bibliographic record
Abstract
One of the bottlenecks in global risk assessment studies is the lack of global sub-national socio-economic datasets spanning the past decades. To bridge this gap, we have compiled 12 global sub-national socio-economic datasets covering cultural diversity, economic conditions, demographics, equity, governance, health, and social well-being. These datasets form a harmonised global socio-economic data cube with annual data for 1990-2021. The data is with either a gridded or sub-national level resolution, except for political stability, which is available only at the national level.We further introduce 'humanscapes,' a novel concept designed to capture complex socio-economic realities at a sub-national level. Humanscapes reflect the interplay of these different datasets, covering over 28,000 administrative units, and are analysed using self-organising maps (SOM) to highlight unique sub-national characteristics. Humanscapes offer a refined method for understanding and mapping societal changes.Our socio-economic data cube enhances precision in global and continental risk assessments by providing comprehensive socio-economic contexts previously unavailable. It thus opens new possibilities in assessing vulnerability to natural hazards on a global scale, aligning with frameworks like the Sendai Framework and the Paris Agreement.
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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 | 0.003 | 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".