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Record W2747416349 · doi:10.5539/ibr.v10n9p116

Can Intelligence Overcome the Effect of Individualism on Economic Development of Regions?

2017· article· en· W2747416349 on OpenAlexvenueno aff
Tian Zengrui, Guillermo Andrés Buitrago, Husam Wahdan

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicEducational Outcomes and Influences
Canadian institutionsnot available
Fundersnot available
KeywordsIndividualismPovertyPer capitaLatin AmericansEconomicsHuman development (humanity)Object (grammar)Sustainable developmentEconomic growthPolitical scienceSociologyComputer scienceMarket economy

Abstract

fetched live from OpenAlex

A clearer understanding of the causal variables of economic development is paramount object of interest for policymakers, researchers and economical analysts. Scholars share the general agreement that economic development is an important tool to alleviate poverty and foster human development. In this study, we explore the role of individualism and intelligence on economic development. These results will show that both individualistic values and national IQs have positive effect on GDP per capita. Moreover, the effect of intelligence appears to be more significant in Latin America.

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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.002
Scholarly communication0.0020.001
Open science0.0000.003
Research integrity0.0000.001
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 head0.155
GPT teacher head0.492
Teacher spread0.337 · 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

Citations1
Published2017
Admission routes1
Has abstractyes

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