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GROWTH AND CONVERGENCE WHEN TECHNOLOGY AND HUMAN CAPITAL ARE COMPLEMENTS

2012· article· en· W2086702917 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

VenueEconomic Inquiry · 2012
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsEconomicsHuman capitalStock (firearms)Endogenous growth theoryPhysical capitalPer capitaCapital deepeningMonetary economicsProduction (economics)ProductivityCapital Consumption AllowanceCapital intensityFinancial capitalMacroeconomicsCapital formationMarket economy

Abstract

fetched live from OpenAlex

This article presents a model of endogenous growth, in which a firm's technology and a country's human capital stock are complementary in the production of output. Production technologies are created by costly research and development (R&D) and are owned by firms that can freely choose where in the world to produce. Both production and R&D have a positive effect on a country's human capital stock. While all countries typically grow at the same rate in the long run, they differ in their levels of human capital, per capita output, and the quality of the technologies that are used in production. A country's relative position in terms of productivity is history dependent. Countries that start out with a lower human capital stock or industrialize later end up with a lower per capita GDP in long‐term equilibrium . ( JEL O4, O33, O47)

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.065
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.053
GPT teacher head0.247
Teacher spread0.195 · 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