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Record W4404406555

Canadian Productivity Growth, Secular Stagnation, and Technological Change

2018· article· en· W4404406555 on OpenAlexaffabout
Secular Stagnation Canadian Productivity Growth, Jon Cohen

Bibliographic record

VenueDOAJ (DOAJ: Directory of Open Access Journals) · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEconomic stagnationProductivityTechnological changeEconomicsSecular variationEconomic geographyEconometricsPolitical scienceMacroeconomicsSociologyDemographyLaw
DOInot available

Abstract

fetched live from OpenAlex

In this article, we show first that the recent slowdown in productivity growth in Canada, similar to that in the United States, can be attributed at least in part to the fall-off in the commercialization of new technologies. Using our bookbased indicators of technological change, we are able to show that this is true for both aggregate measures of technology and, at the disaggregate level, for mechanical/manufacturing and electrical technologies. Our results also indicate that the productivity impact of the slowdown in Canada is much greater on goods-producing industries than it is on services. Second, our latest results suggest that, contrary to the concerns of some that we are entering a new period of secular stagnation characterized by low productivity and economic growth, we are actually on the threshold of significant new technological breakthroughs, associated largely, but not only, with advances in artificial intelligence (AI) and robotics. Provided that Canadian firms adopt these innovations, we can anticipate not a continuation of slow productivity growth but an acceleration.

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)0.009
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.060
Threshold uncertainty score0.433

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0060.018
Science and technology studies0.0030.002
Scholarly communication0.0030.001
Open science0.0010.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0040.000

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 head0.266
GPT teacher head0.444
Teacher spread0.178 · 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 designNot applicable
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

Citations2
Published2018
Admission routes2
Has abstractyes

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