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Record W2019350317 · doi:10.1080/0953531032000056927

The Performance of Service Industries in Canada: A Real Value Analysis

2003· article· en· W2019350317 on OpenAlexaffabout
René Durand, Sylvain Vézina

Bibliographic record

VenueEconomic Systems Research · 2003
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicTransport and Economic Policies
Canadian institutionsInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsOutsourcingProductivityService (business)Tertiary sector of the economyUnit (ring theory)EconomicsRelative priceValue (mathematics)Goods and servicesBusinessCapital (architecture)Capital equipmentIndustrial organizationAgricultural economicsLabour economicsMonetary economicsEconomyMarketingMacroeconomicsStatistics

Abstract

fetched live from OpenAlex

The performance of service industries in Canada has been lower than that of good industries over the last four decades, with noticeable exceptions such as for railways and telecommunication carriers. Service industries were less economically (and technically) efficient in that they generated less output value (quantity) per hour worked (level and growth) or per combined unit of labour and capital (multifactor productivity growth) than good industries. The relative output price of services declined slightly over time compared with goods. At the disaggregated level, changing relative output prices were substantial and proved to be an important factor explaining the relative satisfactory economic performance of many service industries despite their low technical performance. Nevertheless, the output share of service industries increased over that period, sustained, mainly, by the growing recourse of all firms to outsourcing of services.

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)0.004
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.069
Threshold uncertainty score0.500

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0050.016
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.048
GPT teacher head0.256
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

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

Citations4
Published2003
Admission routes2
Has abstractyes

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