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Record W2281778058

Explanations of the Decline in Manufacturing Employment in Canada

2015· article· en· W2281778058 on OpenAlexaboutno aff
Evan Capeluck

Bibliographic record

VenueCSLS Research Reports · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsOutsourcingProductivityLabour economicsEconomicsLiberian dollarCompetition (biology)SlowdownManufacturingManufacturing sectorJob lossBusinessUnemploymentEconomic growth
DOInot available

Abstract

fetched live from OpenAlex

The objective of this report is to examine the reasons for the decline in manufacturing’s employment share in Canada, with particular attention paid to the roles of labour productivity growth, demand-side factors, and outsourcing. The results of the report suggest that above average labour productivity growth explains most of the decline in the manufacturing employment share before 2000, while below-average real output growth explains most of the decline after 2000. The slowdown in real output growth after 2000 reflects the sector’s poor export performance which is related to many factors, including: a loss in cost competitiveness linked to an appreciation of the Canadian dollar; increased competition in the U.S. import market; and a slowdown in domestic demand growth in the United States. However, the story becomes more complicated when manufacturing employment is broken down into its various components. In particular, the evolution of manufacturing employment was, in different periods, largely driven by the fortunes of specific industries.

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)0.003
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 score0.072
Threshold uncertainty score0.522

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.005
Science and technology studies0.0060.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0080.001

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 head0.202
GPT teacher head0.325
Teacher spread0.123 · 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

Citations1
Published2015
Admission routes1
Has abstractyes

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