© 2002 Kluwer Academic Publishers. Printed in the Netherlands. 135 Health profiles of Hamilton: Spatial characterisation of neighbourhoods for health investigations
Bibliographic record
Abstract
factors, spatial analysis This paper is part of a larger research program which employs a mixed-methods approach to study the determinants of health at the local level using specific neighborhoods in Hamilton, Ontario, Canada. In this paper, multivariate, spatial statistical techniques and geographic information systems are used to address questions about the characterization of neighbourhoods, based on socioeconomic determinants of health and risk factors such as smoking. While neighbourhood characterization has been a component of public health surveillance for some time, geostatistical techniques can now be used to derive more accurate representation of neighbourhoods for use in subsequent analysis. We utilize principal components analysis to reduce the data and extract the components that represent the underlying local processes. Principal components are also overlayed on comparative mortality figures to visualize where the socio-demographic determinants of health correspond spatially with mortality patterns. Predicted values from the components are then analysed for spatial clustering using local indicators of spatial association. The findings reveal a pattern of distinct neighbourhoods that will be used in subsequent
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.523 | 0.304 |
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".