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Record W1987308615 · doi:10.1016/j.jom.2006.05.014

Factors affecting the evolution of manufacturing in Canada: An historical perspective

2006· article· en· W1987308615 on OpenAlex
Jaydeep Balakrishnan, Janice B. Eliasson, Timothy R.C. Sweet

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

VenueJournal of Operations Management · 2006
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsPetrel Robertson Consulting (Canada)University of Calgary
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsManufacturingCompetition (biology)BusinessInvestment (military)Government (linguistics)Manufacturing operationsIndustrial organizationMarketingPoliticsEngineeringPolitical scienceManufacturing engineering

Abstract

fetched live from OpenAlex

Abstract This paper examines the factors that influenced developments in industry and manufacturing in Canada from the 17th to the 20th century. Although Canada's abundance of natural resources led to the development of primary industries in the 17th and 18th centuries, the manufacturing industry was not significant until the early 19th century. Four representative manufacturing industries are discussed to illustrate the overall trend in the chronological evolution of Canadian manufacturing in the 19th and 20th centuries. The role and impact of factors such as transportation, electricity, foreign investment, particularly by U.S. entrepreneurs, and government support for industry is reviewed to understand their impact on manufacturing as it has evolved to the present. It appears that these were indeed influential and thus are factors that other countries in a less developed stage of their manufacturing evolution may look to for directions. Our analysis also shows that Canadian manufacturing which began by producing simple items in small volumes due to geographical diversity and the absence of a large market, moved into the mass manufacturing age only in the 20th century. But in the 21st century due to competition from low labour cost countries Canada has moved back to customized manufacturing though in sophisticated goods such as aircraft manufacturing and biotech. While there are bright spots in Canadian manufacturing, recent studies also show that work needs to be done to produce more value added products and ensure Canadian manufacturing competitiveness in the global market place.

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)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.846
Threshold uncertainty score0.999

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 head0.026
GPT teacher head0.213
Teacher spread0.187 · 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