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Record W4367048703 · doi:10.21203/rs.3.rs-2807503/v1

Innovation-driven growth in a multi-country world

2023· preprint· en· W4367048703 on OpenAlexafffund
Till Gross, Paul Klein

Bibliographic record

VenueResearch Square · 2023
Typepreprint
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsCarleton University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsProductivityEconomicsEndogenous growth theoryScale effectsEconomic geographyCross countryDeveloping countryScale (ratio)Demographic economicsEconomic growthHuman capitalGeography

Abstract

fetched live from OpenAlex

Abstract We develop a multi-country model of endogenous growth through innovation with cross-country spillovers. A key feature of our model is that some ideas are globally applicable, while others are of local use only. The main implications of our model are the following. On a balanced growth path (i) all countries integrated into the world economy have a common endogenous rate of productivity growth; (ii) a fully integrated country’s size does not affect its productivity, but for partially integrated countries, productivity is increasing in country size; (iii) other characteristics of a country, such as its research effort, determine the level of productivity; (iv) at a global level, there is a scale effect so that productivity growth increases in the size of the world’s population, but this effect is concave and not linear. We also study transitional dynamics, and find that our framework may help us understand the apparent fall in research productivity in currently rich countries in recent decades. JEL Classification: E62, F43, H21, O3

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)0.002
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.030

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0010.002
Scholarly communication0.0040.003
Open science0.0020.002
Research integrity0.0030.002
Insufficient payload (model declined to judge)0.0090.001

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 head0.218
GPT teacher head0.368
Teacher spread0.150 · 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 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
Published2023
Admission routes2
Has abstractyes

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