Dataset for "A probabilistic social vulnerability index to assess spatio-temporal vulnerability dynamics using Bayesian machine learning"
Bibliographic record
Abstract
This dataset and accompanying Python codes support the published article “A Probabilistic Framework for Spatio-Temporal Social Vulnerability Assessment to Flood Hazards in Canada using Bayesian Machine Learning” by Chakraborty et al. (2026), published in the Journal of Geovisualization and Spatial Analysis. The repository includes geospatial data layers (shapefiles), socioeconomic indicators, and racial/ethnic variables used to construct a probabilistic social vulnerability index through Bayesian machine learning and Monte Carlo simulation. Data preprocessing, spatial analysis, and modeling were conducted using GIS and Python. The Python codes are provided as Jupyter Notebook source files and are specifically designed to support risk analysis at the census dissemination area (DA) geographic level. This dataset is intended to support reproducible research and further applications in disaster risk reduction, dynamic social vulnerability assessment, probabilistic risk analysis, and environmental justice research. All datasets and codes are associated with and derived from the published Springer Nature article and are subject to copyright under Springer Nature. Users are required to provide appropriate reference, and citation to the original publication for any reuse, redistribution, or derivative work. Required citation (APA style):Chakraborty, L., Taherimashhadi, H., Thistlethwaite, J., et al. (2026). A probabilistic framework for spatio-temporal social vulnerability assessment to flood hazards in Canada using Bayesian machine learning. Journal of Geovisualization and Spatial Analysis, 10(3). https://doi.org/10.1007/s41651-025-00247-y
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.024 |
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".