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Record W2199673366 · doi:10.25336/p6t31v

An Analysis of Socio-Economic Strains and Population Gains: Urban and Rural Communities of Canada 1981-2001

2007· article· en· W2199673366 on OpenAlexafffundvenueabout
Fernando Mata, Ray D. Bollman

Bibliographic record

VenueCanadian Studies in Population · 2007
Typearticle
Languageen
FieldSocial Sciences
TopicUrban, Neighborhood, and Segregation Studies
Canadian institutionsStatistics CanadaEmployment and Social Development Canada
FundersHuman Resources and Skills Development CanadaUniversity of Alberta
KeywordsCensusGeographyPopulationUnemploymentPovertyAmerican Community SurveyVariety (cybernetics)Socioeconomic statusSocioeconomicsSubdivisionDemographic analysisEarningsEconomic growthRegional scienceEconomic geographyDemographyEconomicsSociologyStatistics

Abstract

fetched live from OpenAlex

Important demographic shifts have occurred in Canada in the last decades. As a consequence of these shifts, many geographical communities have won or lost substantial number of residents between 1981 and 2001. Using the CCS (consolidated census subdivision) data set of the Agriculture Division of Statistics Canada, the paper explores the linkages between socio-economic strains and population changes affecting communities in a variety of regional and provincial contexts. A total of 2,607 rural and urban consolidated census subdivisions were examined across five census periods. Quasi simplex structural equation models using unemployment, earnings and poverty as indicators were tested on a variety of communities located in various OECD regions and provinces. Although the predictive power of strains on population gains was found to be limited in the models, a higher level of strain was persistently found to be negatively associated with population gains regardless of regional and provincial groupings of communities. Socio-economic strains were also observed to be relatively stable over time across a variety of geographies.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.164
Threshold uncertainty score0.461

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 studies0.0010.000
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 head0.059
GPT teacher head0.346
Teacher spread0.288 · 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".

Quick stats

Citations1
Published2007
Admission routes4
Has abstractyes

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