Considering Risk Locations When Defining Perturbation Zones for Geomasking
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Bibliographic record
Abstract
Geomasking techniques are commonly used to mask the true location information of cases by introducing noise into location data. This study seeks to improve the spatially adaptive random perturbation (SARP) geomasking method by using the actual distribution of the residential addresses (or “risk location”) rather than the people (or “risk population”) to define a perturbation zone. The procedure used in the study also employs a “donut-shaped” perturbation zone, rather than the traditional “pancake-shaped” zone, when displacing a case. The effectiveness of the proposed geomasking methods is assessed in terms of their potential to control for location re-engineering and their ability to maintain the point patterns embedded in the real distribution. The authors conclude that SARP geomasking using the distribution of actual street addresses protects privacy more effectively than geomasking based on population size; the different SARP techniques do not significantly change the clustering patterns on a global level, but the geomasked data tend to be more clustered than the real case distribution.
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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 | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it