An Open-Source Software for Calculating Indices of Urban Residential Segregation
Bibliographic record
Abstract
The aim of this article is to introduce a new stand-alone application—Geo-Segregation Analyzer—that is capable of calculating 43 residential segregation indices, regardless of the population groups or the metropolitan region under study. In practical terms, the user just needs to have a Shapefile geographic file containing counts of population groups that differ in ethnic origin, birth country, age, or income across a metropolitan area at a small area level (e.g., census tracts). Developed in Java using the GeoTools library, this free and open-source application is both multiplatform and multilanguage. The software functions on Windows, Mac OS X, and Linux operating systems and its user interface currently supports 10 languages (English, French, Spanish, Catalan, German, Italian, Portuguese, Creole, Vietnamese, and Chinese). The application permits users to display and manipulate several Shapefile geographic files and to calculate 19 one-group indices, 13 two-group indices, 8 multigroup indices, and 3 local measures that could be mapped (location quotient, entropy measure, and typology of the ethnic areas proposed by Poulsen, Johnson, and Forrest).
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.034 | 0.015 |
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".