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Record W2805684079 · doi:10.3846/20294913.2017.1280557

TECHNOLOGICAL SOURCES OF ECONOMIC GROWTH IN EUROPE AND THE U.S.

2018· article· en· W2805684079 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTechnological and Economic Development of Economy · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Waterloo
FundersJunta de AndalucíaUniversidad de Málaga
KeywordsEconomicsTechnological changeTechnical changeInvestment (military)Growth accountingProductivityTechnical progressHuman capitalTotal factor productivityGeneral equilibrium theoryCapital accumulationCapital (architecture)Growth modelMacroeconomicsEconomic growth

Abstract

fetched live from OpenAlex

This paper assesses the role of different sources of technological change as determinants of economic growth in a group of selected OECD countries during the period 1980–2010. We consider three different sources of growth: neutral technical change associated with Total Factor Productivity, investment-specific technical change (ISTC) embodied in capital assets, and improvements in the quality of labor services generated by human capital accumulation. The contribution to growth of each of these sources is computed using two different approaches: the standard (statistical) growth accounting and the structural growth decomposition obtained from a general equilibrium growth model. We found that the effect of ISTC dominates that of neutral technology and human capital in all of the countries considered. On average, more than 50% of productivity growth is explained by ISTC. Contributions to growth from ICT and non-ICT technical change are in general of similar magnitude.

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.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
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 score0.081
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.004
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.025
GPT teacher head0.194
Teacher spread0.169 · 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