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Record W2755503889 · doi:10.1504/ijtip.2017.10006430

Using patents and innovation strings to anticipate the next Kondratieff long waves

2017· article· en· W2755503889 on OpenAlexaboutno aff
Luís Roberto Vega-González

Bibliographic record

VenueInternational Journal of Technology Intelligence and Planning · 2017
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Development and Digital Transformation
Canadian institutionsnot available
Fundersnot available
KeywordsKondratiev waveAnalogyQuarter (Canadian coin)EconomicsWork (physics)Keynesian economicsPhysicsHistory

Abstract

fetched live from OpenAlex

The theory of Kondratieff's long waves (LWs) has been discussed by socio-economic scientists for nearly a century. Recent works have proposed that since the end of the 19th century there have been at least five well-determined long waves (K-1 to K-5) in the world economy, all of them powered by technology innovations that have fostered economic development in the world nations and transformed society. As the sixth long wave is beginning in the first-quarter of the 21st century, the objective of this work is to propose a model of a physical fission reactor analogy methodology, developed to obtain the string of disruptive innovations that triggered and fed the world economy oscillations in each LW. In addition, we will use the innovation strings found to foresee which may be the characteristics and the technology applications for the K-6 and K-7 long waves.

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)0.008
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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0010.003
Scholarly communication0.0020.007
Open science0.0010.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.225
GPT teacher head0.339
Teacher spread0.114 · 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
Published2017
Admission routes1
Has abstractyes

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