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Record W3121384106 · doi:10.20955/wp.2013.025

The Quantitative Importance of Openness in Development

2013· report· en· W3121384106 on OpenAlexaff
B. Ravikumar, Raymond Riezman, Wenbiao Cai

Bibliographic record

Venuenot available
Typereport
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic theories and models
Canadian institutionsUniversity of Winnipeg
Fundersnot available
KeywordsOpenness to experiencePsychologySocial psychology

Abstract

fetched live from OpenAlex

This paper deals with a classic development question: how can the process of economic development -transition from stagnation in a traditional technology to industrialization and prosperity with a modern technology -be accelerated?Lewis (1954) and Rostow (1956) argue that the pace of industrialization is limited by the rate of capital formation which in turn is limited by the savings rate of workers close to subsistence.We argue that access to capital goods in the world market can be quantitatively important in speeding up the transition.We develop a parsimonious open-economy model where traditional and modern technologies coexist (a dual economy in the sense of Lewis (1954)).We show that a decline in the world price of capital goods in an open economy increases the rate of capital formation and speeds up the pace of industrialization relative to a closed economy that lacks access to cheaper capital goods.In the long run, the investment rate in the open economy is twice as high as in the closed economy and the per capita income is 23 percent higher.

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)0.010
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: Other · Consensus signal: none
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.005
Scholarly communication0.0030.005
Open science0.0000.004
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.116
GPT teacher head0.282
Teacher spread0.166 · 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
GenreOther

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
Published2013
Admission routes1
Has abstractyes

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