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Record W4392656378 · doi:10.1111/cag.12902

Revisiting small and mid‐sized cities in Canada: Old questions, new challenges*

2024· article· en· W4392656378 on OpenAlexaffvenueabout
Thi‐Thanh‐Hiên Pham, Jeffrey Biggar, Yolande Pottie‐Sherman

Bibliographic record

VenueCanadian Geographies / Géographies canadiennes · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicUrban Planning and Governance
Canadian institutionsMemorial University of NewfoundlandDalhousie UniversityUniversité du Québec à Montréal
Fundersnot available
KeywordsNova scotiaHumanitiesLibrary scienceGeographyArtArchaeology

Abstract

fetched live from OpenAlex

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,

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.579
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
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.017
GPT teacher head0.210
Teacher spread0.194 · 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

Citations4
Published2024
Admission routes3
Has abstractyes

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