Revisiting small and mid‐sized cities in Canada: Old questions, new challenges*
Bibliographic record
Abstract
MATTER MORE THAN EVERSmall and mid-sized cities (SMCs) are increasingly where people in Canada "live, work, and play" (Statistics Canada, 2022, 2023).In 2021, for example, eight of the top ten fastest growing Canadian census metropolitan areas were mid-sized cities-including Kelowna, Halifax, Guelph, and Moncton (Statistics Canada, 2021).There is a growing recognition that SMCs serve as repositories of innovation in urban theory and policy, offering distinct contexts for technology, culture, creativity, and collaboration, and potentially holding the key to Canada's collective well-being (e.g., Flatt, 2018; SDG Cities, 2021).SMCs, however, grapple with the global forces which channel resources to larger cities (Barber et al., 2023;Filion, 2023;Grant et al., 2019)."Slow growth," "shrinking," "less favoured," "peripheral," "intermediate," and "second tier" or "third tier" are all terms reflecting Canada's diverse urban geographies and are fundamentally about the broader socio-spatial and power relationships that disadvantage SMCs (e.g., Hall & Hall, 2008;Hartt, 2021).But not all SMCs' problems are the same, and neither are their successes.There is thus a critical need to think about the role of SMCs in building places, economies, and communities.This special section for Canadian Geographies/Gographies canadiennes examines some of the most pressing challenges facing SMCs in Canada, providing a conceptual framework for understanding these cities across the disciplinary boundaries of geography, planning, urban studies, and environmental sciences.Its impetus was a May 2022 summer school on SMCs in Victoriaville, Quebec.Organized by the Canadian Research Chair of small and mid-sized cities in transformation (at UQAM, Quebec) and the Adaptive Cities and Engagement Space (at Memorial University, Newfoundland and Labrador), this event emphasized the need for greater research collaboration on SMCs between different disciplines and among Canadian universities.We use the label "SMCs"-which in Canada generally refers to a vast and dynamic urban spectrum of cities with more than 10,000 and fewer than 500,000 residents-while foregrounding the limitations of such numeric definitions, following previous researchers (Bruneau, 2000;Hartt & Hollander, 2018;Seasons, 2003).For example, what commonalities exist between a population centre of 10,000 and a city approaching the ceiling of 500,000?Others, like Tassonyi (2017) stretch the upper limit of a Canadian mid-sized city to 2 million!There is enormous diversity even among cities in the same province of similar population size, for example the cities of Kingston and Sudbury in Ontario (Filion, 2023).Numeric definitions also generally ignore how "smallness is bound up with particular ways of acting, self-images, structures of feeling, senses of place, aspirations" (Bell & Jayne, 2009, p. 690).Focusing on size alone also ignores the role that these cities play in their region (Carrier & Demazire, 2012;Hinderink & Titus, 2002;Proulx, 2006;Rochefort et al., 2023), and yet researchers invariably consider population as a bellwether for differentiating SMCs from their large city counterparts (Grant et al., 2019).What does the term "SMC" do for us besides acting as a beacon for other researchers interested in similar themes?We see the concept of SMCs as part of the broader intellectual project challenging the longstanding preoccupation of urbanists with global cities (Bell & Jayne, 2009;Kanai et al., 2018;Wagner & Growe, 2021).While interest in Canadian SMCs is growing, for years Canadian urbanists repudiated them in favour of Toronto, Vancouver, and Montreal-colloquially known as the "MTV cities" (Hall & Hall, 2008;Hartt & Hollander, 2018).Theoretically, this special section aligns itself with the global movement to decentre urban studies, rejecting the idea that there is a "singular urban story," acknowledging the significance of the "ordinary city" while generating urban theory from the "margins" (e.g., Derickson,
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".