Bibliographic record
Abstract
Abstract Census data has been widely used for community evaluation based on demographic and socioeconomic variables. However, the analysis is typically associated with specific areal units and the results often change when the size of the census configuration changes leading to scale distortions. Various approaches such as optimal zoning systems and multivariate statistical analysis have been developed to address the scale problem. But limitations in these approaches have led to the use of non-statistical methods to tackle the scale problem. This study combines a non-statistical method with descriptive statistical measures to develop a rough sets approach to constructing a census-based deprivation index (DI) and to determine its relationship to a recent immigrant population using the 2001 Canadian census. Application of the approach in the Greater Vancouver Regional District shows that rough sets can stabilize relationships for spatially grouped census data by mini-mizing scale distortions. Scale sensitivity measures are also estimated to translate DI relationships across three census configurations. The rough sets approach is suitable for areal data analysis because it is resistant to nonlinearity, outliers, and assumes no prior relationship between variables.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 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".