The boundaries of suburban discontent? Urban definitions and neighbourhood political effects
Bibliographic record
Abstract
Residents of city and suburban neighbourhoods have diverged in the way they vote, with inner‐city dwellers preferring political parties on the left while suburbanites increasingly vote for parties on the right. Yet it is not clear whether such a division is more evident between residents of central and suburban municipalities (the jurisdictional hypothesis), or between residents of neighbourhoods differentiated by urban form and, by assumption, lifestyle (the morphological hypothesis). While there are clear reasons for the predominant reliance on municipal differences in research based in the U.S. and other countries, it is not evident that these reasons apply in the Canadian context. This article examines how urban boundaries articulate electoral differences between metropolitan residents in Canada's three largest urban regions, using aggregate election data for federal elections between 1945 and 2000, survey data from the 2000 Canada election study and a series of indices developed by the author. It is found that while trends towards city–suburban polarization are similar regardless of the boundaries used to define the zones, in the Canadian case the results are stronger and more significant when boundaries based on urban form (between pre‐and post‐war development) are employed. The implications of these results for the relationship between urban space and political values in Canadian cities are then discussed.
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 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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".