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Record W2008274038 · doi:10.1080/0003684032000125051

Induced innovation: an empirical test

2003· article· en· W2008274038 on OpenAlexaboutno aff
Isabelle Armanville, Peter Funk

Bibliographic record

VenueApplied Economics · 2003
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsEconomicsEconometricsDependency (UML)Test (biology)Alternative hypothesisElectricityNull hypothesis

Abstract

fetched live from OpenAlex

A method is developed to empirically test the hypothesis of induced innovation as it has been specified and used in the theoretical literature. A strong and a weak version of the hypothesis is tested using sectorial data from the USA, Canada, Germany, France and the UK. The strong version tests for the exact dependency of the relation between the change in factor-productivities on the one hand and relative prices and actual factor-productivities on the other hand. The weak version only tests for the direction of this dependency. In all countries the weak hypothesis is accepted in all sectors except in ‘electricity, gas, and water’. The strong hypothesis is accepted in about half of all sectors. It is rejected only in sectors, in which the degree to which progress is intentional is low.

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)0.028
metaresearch head score (Gemma)0.181
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: Empirical
Teacher disagreement score0.029
Threshold uncertainty score0.148

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0280.181
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0040.005
Science and technology studies0.0010.005
Scholarly communication0.0040.005
Open science0.0030.003
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0290.002

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 head0.072
GPT teacher head0.247
Teacher spread0.174 · 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

Citations11
Published2003
Admission routes1
Has abstractyes

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