JOURNAL ARTICLE
Institutional Change as Urban Theory: The Fall of At-Large Politics in Calgary, Alberta.
Published In: Urban History Review / Revue d'Histoire Urbaine, 2026, v. 54, n. 1. P. 37 1 of 3
Database: America: History and Life with Full Text 2 of 3
Authored By: Lucas, Jack 3 of 3
Abstract
This article examines the institutional reform of Calgary, Alberta's municipal electoral system during the early post-war period, focusing on the city's transition from a long-standing proportional representation (PR), at-large, partisan system to a single-member majoritarian ward-based system by 1977. Using quantitative electoral data and qualitative archival and newspaper sources, it traces how shifts in electoral competition—particularly the decline of Labour candidates and rise of Independents—alongside population growth and changing local political theories about the purpose of municipal representation, drove reform debates and outcomes. The article highlights how moments of institutional change reveal the implicit theories held by local political elites about urban politics, showing that Calgary's reform process was shaped by both long-term trends and unexpected events, and that evolving beliefs about municipal politics influenced the adoption of geographic ward representation over city-wide proportionality. This case study underscores the importance of local institutional configurations and political actors' own understandings in shaping urban democratic reforms in Canadian cities.
Additional Information
- Source:Urban History Review / Revue d'Histoire Urbaine. 2026/03, Vol. 54, Issue 1, p37
- Document Type:Article
- Subject Area:Geography and Cartography
- Publication Date:2026
- ISSN:0703-0428
- DOI:10.3138/uhr-2024-0027
- Accession Number:192182935
- Copyright Statement:Copyright of Urban History Review / Revue d'Histoire Urbaine is the property of University of Toronto Press and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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