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Record W7101231809

Evolving Business Centres in Canada: The Establishment versus The Next Wave

2015· article· en· W7101231809 on OpenAlexaboutno aff

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic impact analysisMetropolitan areaCore (optical fiber)Regional developmentDistribution (mathematics)City region
DOInot available

Abstract

fetched live from OpenAlex

Regional economic disparities are characteristic of Canada. Southern Ontario and Quebec have long been the home of many of the businesses that dominate the country’s economy, while business centres in the other regions of the country have played a lesser role in all but a few economic sectors. This paper studies the evolution of Canada’s system of business centres by contrasting the locational patterns of headquarters for two groups of businesses: the largest businesses in the country, the Canadian Establishment, and the fastest-growing businesses in the country, the Next Wave. The results show that while the country’s core region dominates both groups, the Next Wave is most highly attracted to suburban locations in the national core. The Canadian Establishment is dominated by central-city locations in Toronto and Montreal, as expected, but is also more spatially dispersed than the Next Wave at a provincial level of analysis. The paper provides a number of perspectives on these spatial distributions and suggests that the findings have meaning for the further development of business location theory. Key Words: location theory, quaternary location, corporate headquarters, economic development, establishment, next wave. Regional economic disparities are an enduringcharacteristic of Canada. In this sense, Canada is byno means unique, as countries worldwide have long grappled with the impacts of regional economic variations on the health and wellbeing of their cities and people. Italy’s industrialized and prosperous north contrasts with

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.761
Threshold uncertainty score0.358

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
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 head0.084
GPT teacher head0.198
Teacher spread0.114 · 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 designTheoretical or conceptual
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

Citations0
Published2015
Admission routes1
Has abstractyes

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