Model and code for "The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis"
Bibliographic record
Abstract
This repository contains the data, model code, and initial values for a spatial factor analysis model described in: The spatial structure of socioeconomic disadvantage: a Bayesian multivariate spatial factor analysis. International Journal of Geographical Information Science. The International Journal of Geographical Information Science is available at: https://www.tandfonline.com/toc/tgis20/current Abstract:Neighborhood socioeconomic disadvantage is a measure of socio-spatial inequality that has been shown to be associated with a variety of social, economic, and health outcomes. Existing studies that explore the local patterning of disadvantage often construct composite indices that summarize the interactions between multiple dimensions of social status, but do not consider if, and how, disadvantage exhibits spatial structure. This study applies a Bayesian multivariate factor analytic modeling approach to examine the spatial structure of socioeconomic disadvantage in Toronto, Canada. Socioeconomic disadvantage is modeled as an area-based composite index associated with three variables measuring low income, low educational attainment, and low occupational status, and a series of models with different assumptions regarding the spatial structure of disadvantage are compared. The best-fitting model shows that the prevalence of low-income households has the strongest positive association with disadvantage and that spatial clustering is three times more important than spatial heterogeneity for explaining the spatial structure of disadvantage. The implications of this study for analyzing multivariate spatial data and for understanding the interactions amongst multiple dimensions of disadvantage are discussed.
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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.244 | 0.059 |
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".