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Record W1583847179

Industry Mix, Plant Turnover and Productivity Growth: A Case Study of the Transportation Equipment Industry in Canada

2011· article· en· W1583847179 on OpenAlexaffabout
Kelvin Ka Yin Chan, Jianmin Tang, Wulong Gu

Bibliographic record

VenueRePEc: Research Papers in Economics · 2011
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicGlobal trade and economics
Canadian institutionsStatistics CanadaInnovation, Science and Economic Development Canada
Fundersnot available
KeywordsProductivityRestructuringSlowdownMultifactor productivityEconomicsAgricultural economicsLabour economicsTotal factor productivityInternational tradeEconomic growthFinance
DOInot available

Abstract

fetched live from OpenAlex

The transportation equipment industry is one of the few Canadian industries that is as productive as its U.S. counterpart. However, labour productivity growth in the Canadian transportation equipment industry declined from 4.5 per cent per year in 1981-2000 to 1.7 per cent per year in 2000-2007. This article investigates whether restructuring and the reallocation of output and resources within the industry after 2000 contributed to this decline. It shows that the dramatic decline in productivity growth was mainly due to the slowdown in productivity growth in sub-industries, which can largely be traced to the decline in labour productivity growth of continuing plants. Finally, the article shows that even if the Canadian industry mix were the same as the U.S. mix, the productivity growth profile of the Canadian transportation equipment industry would not change.

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)0.000
metaresearch head score (Gemma)0.001
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 score0.034
Threshold uncertainty score0.248

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0030.007
Science and technology studies0.0040.001
Scholarly communication0.0020.001
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 head0.093
GPT teacher head0.248
Teacher spread0.155 · 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

Citations5
Published2011
Admission routes2
Has abstractyes

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