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 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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".