Creating Neighborhood Typologies of GIS-Based Data in the Absence of Neighborhood-Based Sampling: A Factor and Cluster Analytic Strategy
Bibliographic record
Abstract
This article describes an innovative means of identifying a neighborhood typology that can be used for analyses of individual-level data that were not obtained through neighborhood-based sampling. A two-step approach was employed. First, exploratory factor analysis was used to reduce the number of neighborhood indicators to five clear factors of neighborhood characteristics. Second, a cluster analytic procedure was used to identify neighborhood types based on the five factors. These analyses resulted in a parsimonious solution of five distinct neighborhood clusters, or types, that constituted a manageable number of categories that could be used for future analyses of individuals grouped within neighborhood types. This method is a promising way to conduct neighborhood impact analyses that maximize the ability of researchers to characterize neighborhoods accurately (without sampling at the neighborhood level) while retaining the ability to conduct analyses of participants grouped within types of neighborhoods.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".