The compactness of federal electoral districts in Canada in the 1980s and 1990s: an exploratory analysis
Bibliographic record
Abstract
Considerations of the compactness and aesthetics of electoral districts have loomed large in the litigation surrounding the last round of US redistricting. For a variety of reasons, we expect that there will be increasing pressure on Canadian electoral cartographers to provide opportunities for protected minorities to be represented in the country's federal legislature. As a result, we expect that future electoral maps may well embody a tradeoff between compactness and other representational and cartographic desiderata. We look for evidence of this in an exploratory analysis of the last two federal electoral maps (adopted in 1987 and 1996) and in so doing we offer the first country‐wide assessment of the compactness of Canadian federal electoral districts (FEDs). The results demonstrate the importance of natural boundaries in the achievement of district compactness. Strong evidence of a decline in the compactness of FEDs between the two maps is not forthcoming, however. Thus there is relatively little sign that Canadian electoral districts will be open to the kind of aesthetically‐based legal challenges that American Congressional Districts faced in the 1990s. However, the analyses we report establish an important baseline against which the next electoral map, to be produced following the 2001 census, can be compared.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.010 | 0.021 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".