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Record W2982042461 · doi:10.1353/gpr.2019.0034

Small Cities, Big Issues: Reconceiving Community in a Neoliberal Era ed. by Christopher Walmsley and Terrance Kading

2019· article· en· W2982042461 on OpenAlexaboutno aff
Mervyn Horgan

Bibliographic record

VenueGreat Plains research · 2019
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicRural development and sustainability
Canadian institutionsnot available
Fundersnot available
KeywordsSociologyScale (ratio)Political scienceEconomic historyGeographyHistoryCartography

Abstract

fetched live from OpenAlex

Reviewed by: Small Cities, Big Issues: Reconceiving Community in a Neoliberal Era ed. by Christopher Walmsley and Terrance Kading Mervyn Horgan Small Cities, Big Issues: Reconceiving Community in a Neoliberal Era. Edited by Christopher Walmsley and Terrance Kading. Edmonton: AU Press, 2018. ix + 334 pp. Figures. $37.95 paper. The social scientific study of small cities is woefully underdeveloped. It's a curious foible of social research that work on small cities tends to be more the domain of rural researchers who scale up from small rural communities to small cities, and less the domain of urban researchers who tend instead to train their vision on big issues facing big cities, leaving aside entirely the social dynamics of small cities. Consequently, studies of both small rural communities and of big cities abound, but little work focuses on the specificity of small cities. This tendency is most certainly a loss for urban researchers (and a mea culpa is in order here), as many of the processes at play in large metropolises are equally, if not more, tangible and visible in smaller cities. Moreover, as the contributors to this highly readable and thoughtfully edited volume show, all too often small cities are collateral damage when broad-scale structural transformations—rent primarily by neoliberal economic policies—are underway. As the editors note in their introduction, "small is . . . a relative term," thus making a universally applicable definition near impossible. As the majority of the chapters in the collection are based on case studies from small Canadian cities, the editors pragmatically adopt a somewhat fluid definition treating small cities as those with a population between 10,000 and 100,000, with some wiggle room at each end. Given the relative underdevelopment of what we might call the "small cities subfield," kudos are due to the editors of this fascinating collection for bringing together scholars and practitioners researching and working across a wide range of fields including sociology, social work, political science, and mental health. Each chapter shows that one barely needs to scratch the surface in small cities to very quickly reveal big issues at play. Across 12 well-written and thematically coherent chapters, I learned about homelessness, illicit drug use, sex work, queerness, deinstitutionalization, incarceration and parole, aboriginal peoples, planning and governance, immigrant settlement, poverty reduction, and community empowerment. Chapter by chapter it became clearer that small cities are sites of "serious inequities and social tensions," which as most authors demonstrate, quite clearly derive from state disinvestment and increasingly punitive social policy. One minor gripe with this book: the absence of an index makes quick access to specific topics more difficult, though to be fair, in addition to the reasonably priced paper copy of the book, AU Press is to be commended for adopting a hybrid publishing model that also makes this book freely available as an open access digital download, so terms can be searched on the portable document format (PDF). Overall, Small Cities, Big Issues advances a critical and timely analysis of the state of small Canadian cities in the neoliberal era. In contemporary social science, I sometimes feel like "neoliberalism" is a catchall buzzword or an inconsistently applied analytic term, but as the research on small cities reported in this book very clearly demonstrates, neoliberalism's everyday effects are consistently devastating to communities. [End Page 169] Mervyn Horgan Department of Sociology and Anthropology University of Guelph, Canada Copyright © 2019 Center for Great Plains Studies, University of Nebraska–Lincoln

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.144
Threshold uncertainty score0.991

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.073
GPT teacher head0.291
Teacher spread0.217 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

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Citations0
Published2019
Admission routes1
Has abstractyes

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