MétaCan
Menu
Back to cohort
Record W1978870925 · doi:10.4312/dela.21.19.223-231

Specialization in services: a Canadian example

2004· article· en· W1978870925 on OpenAlexaboutno aff
James W. Simmons

Bibliographic record

VenueDela · 2004
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicRegional Economics and Spatial Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsTertiary sector of the economyPopulationAgriculturePer capitaEconomic geographyPer capita incomePrimary sector of the economyService (business)GeographyPrivate sectorPublic sectorBusinessEconomic growthRegional scienceEconomicsEconomy

Abstract

fetched live from OpenAlex

In modern urban systems the economic growth of cities is largely driven by services. In many regions employment growth in primary and secondary activities is close to zero, or even negative. Growth depends on the ability to attract jobs in the services. This study explores the pattern of specialization in various service activities for 159 Canadian urban areas in 1996, as the basis for a series of maps for the Atlas of Canada. The hierarchical specialization is evaluated for each service sector by computing a regression model of ser-vice employment as a function of urban population and income per capita. The rapidly growing business and financial services are the most strongly oriented to larger cities. The horizontal specialization is measured as residuals from the regressions. Strong regional differences contrast the central place roles of agricultural communities with the more loca-lized markets of resource and manufacturing centres. Public sector decisions about the loca-tion of major health and education facilities complement the choices of the private sector.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.050
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.011
Science and technology studies0.0060.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0070.000

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 head0.029
GPT teacher head0.186
Teacher spread0.158 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2004
Admission routes1
Has abstractyes

Explore more

Same venueDelaSame topicRegional Economics and Spatial AnalysisFrench-language works237,207