Segregation Analyzer: a C#.Net application for calculating residential segregation indices
Bibliographic record
Abstract
Segregation indices are today well known and increasingly used in urban studies. However, in the absence of specialized computer tools, calculating segregation indices soon becomes a long and complicated process. The odd free applications designed to calculate indices are implemented in geographic information systems (ArcInfo, ArcView and MapInfo). Users wishing to calculate indices by way of these applications must have the GIS software that contains the application and also a sufficient understanding of how to use geographic information systems—two conditions that can limit the use and correspondingly, broad access to residential segregation indices. To remedy this situation, we propose an independent and free application developed in C#.Net called Segregation Analyzer that allows some forty segregation indices (unigroup, intergroup, and multigroup) to be calculated quickly and easily, regardless of the data or city being studied. This application can be downloaded free of charge from the Spatial Analysis and Regional Economics Laboratory —SAREL— Web site (http://laser.ucs.inrs.ca/EN/Download.html).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.081 | 0.033 |
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".