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The compactness of federal electoral districts in Canada in the 1980s and 1990s: an exploratory analysis

2001· article· en· W2061531959 on OpenAlexvenueaboutno aff
Paul Bélanger, Munroe Eagles

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2001
Typearticle
Languageen
FieldSocial Sciences
TopicPhilippine History and Culture
Canadian institutionsnot available
Fundersnot available
KeywordsCompact spaceRedistrictingLegislatureCensusGerrymanderingExploratory analysisExploratory researchPolitical scienceRegional sciencePublic administrationGeographyLawSociologyPoliticsComputer sciencePopulationSocial scienceData scienceMathematicsDemography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.247
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.012
Science and technology studies0.0020.002
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.012
GPT teacher head0.215
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations7
Published2001
Admission routes2
Has abstractyes

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