Can Geographically Weighted Regressions Improve Regional Analysis and Policy Making?
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Policy design in a regional context requires explicit recognition of spatial heterogeneity in community characteristics as well as in the heterogeneity of how these characteristics impact the target variables. By providing only a “global” measure for the entire space, standard approaches such as ordinary least squares or (most) spatial econometric models tend to compromise spatial heterogeneity in favor of average estimates and efficiency. More assessment is needed of whether the gains of simplicity and statistical efficiency offset the losses from ignoring spatial heterogeneity. Using data for about 1,900 rural Canadian communities as a backdrop, the authors address this issue using a geographically weighted regression approach. The authors find that for about two-thirds of the variables, standard approaches would have significantly understated the spatial differences in the impact of selected variables. Standard analysis would not have uncovered this information, suggesting that subsequent policy inferences would be poorly suited to many local settings.
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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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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