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Record W4411627871 · doi:10.1787/4b014905-en

Productivity among firms patenting in fourth industrial revolution technologies

2025· report· en· W4411627871 on OpenAlexaboutno aff
Flavio Calvino, Antoine Dechezleprêtre, Hélène Dernis, Alzbeta Vitkova

Bibliographic record

VenueOECD science, technology and industry working papers · 2025
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicFirm Innovation and Growth
Canadian institutionsnot available
Fundersnot available
KeywordsProductivityIndustrial RevolutionBusinessIndustrial organizationEconomicsAgricultural economicsGeographyEconomic growth

Abstract

fetched live from OpenAlex

This study analyses the links between patenting activity in technologies related to the Fourth Industrial Revolution (4IR) and firms’ performance, as measured by labour productivity, multi-factor productivity, value added and employment, with a focus on companies operating in Canada. The paper provides a descriptive analysis of global patenting activity in 4IR technologies, comparing Canada vis à vis other countries. Canadian resident applicants have significantly developed innovations in 4IR technologies, but at a slower pace compared to global trends. Canadian resident innovations tend to be more original and radical. Econometric analysis is conducted using confidential firm-level data from Statistics Canada alongside matched patent-to-company data from ORBIS©. The results show that firms filing 4IR patents are larger and more productive on average. In the Canadian dataset, as firms develop new 4IR patents, they increase their relative productivity, while in both the Canada and ORBIS© samples, they increase their employment and value added relative to other firms.

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 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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.774
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0080.008
Science and technology studies0.0010.003
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0040.004
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 head0.063
GPT teacher head0.253
Teacher spread0.191 · 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; both teacher heads agree on what is shown here.

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
Published2025
Admission routes1
Has abstractyes

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