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Record W1718188162

Knowledge Economics role in explaining growth and innovation

2008· preprint· en· W1718188162 on OpenAlexaboutno aff
Bhekuzulu Khumalo

Bibliographic record

VenueMunich Personal RePEc Archive (Ludwig Maximilian University of Munich) · 2008
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Theory and Institutions
Canadian institutionsnot available
Fundersnot available
KeywordsDiversity (politics)Point (geometry)EconomicsNeoclassical economicsSimple (philosophy)Innovation economicsPower pointKnowledge economyPower (physics)Positive economicsEconomyEpistemologyPolitical scienceLawPsychologyMathematics
DOInot available

Abstract

fetched live from OpenAlex

This paper is written to show that there is a definite model that has been developed that explains the role of innovation to economic growth. This paper is based on the theorem that was built up in the paper that I wrote in 2007 entitled “Point X and the Economics of Knowledge”, as well as the so far unpublished papers concerning the long and short term properties of knowledge. This paper shall us the short term properties of knowledge to explain the relationship between growth and Knowledge. Stuart Kauffman of the university of Calgary believes that “Conventional economic theories about growth and the evolution of future wealth may be inadequate. We need a theory and historical examination of the growth of the actual economic web and of whether, in a supracritical economy, a sufficiently high diversity of the web autocatalytically drives its own growth. Furthermore, we need to understand the mutually and collectively cross-enhancing power of complementary technologies, regulatory structure and attraction of consumers in the creation of wealth.” I say this is wrong, the paper “Point X and the Economics of Knowledge”, gives an excellent framework to answer these questions. This paper will delve to be as simple as possible.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
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 head0.038
GPT teacher head0.208
Teacher spread0.170 · 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 teacher head, not a consensus.

Study designTheoretical or conceptual
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

Citations0
Published2008
Admission routes1
Has abstractyes

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