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Record W2964371783 · doi:10.1111/cico.12424

Can Rust Belt or Three Cities Explain the Sociospatial Changes in Atlantic Canadian Cities?

2019· article· en· W2964371783 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.
aboutThe title or abstract carries a Canadian signal from the geographic lexicon.

Bibliographic record

VenueCity and Community · 2019
Typearticle
Languageen
FieldSocial Sciences
TopicUrbanization and City Planning
Canadian institutionsDalhousie UniversityUniversity of TorontoMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsGeographyRacializationCensusEconomic geographyDeindustrializationPolitical sciencePoliticsSociologyDemographyPopulation

Abstract

fetched live from OpenAlex

Research on American secondary cities has largely focused on so–called “rust belt” cities and has found that they tend to have economic stagnation, racialization, and urban decay in their urban cores occurring after economic crises. Most urban research on Canadian cities has, by contrast, focused on the country's largest cities, Toronto, Montreal, and Vancouver, and has found that urban cores are getting richer, less diverse, and undergoing infrastructural improvements. We examine each model by looking at four secondary Atlantic Canadian cities (Halifax, Moncton, St. John's, and Charlottetown) that all faced major economic crisis in the 1990s to see whether these models can explain the sociospatial changes occurring in them. Analysis of 1996 and 2006 Canadian Census data finds unlike “rust belt” cities or changes seen in larger Canadian cities, there is no clear sociospatial concentration of change. Rather, change is seen through “hot spots” of economic and physical characteristics of neighborhoods.

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.

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 categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.178
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.270
Teacher spread0.208 · 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