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Record W2605973568 · doi:10.18374/cbr-2-3.6

MINT COUNTRIES: HOW �SWEET� IS THEIR FUTURE?

2014· article· en· W2605973568 on OpenAlexaff
A. Barker

Bibliographic record

VenueCalifornia Business Review · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsYork University
Fundersnot available
KeywordsClosing (real estate)Join (topology)PoliticsIndex (typography)EconomyBusinessPolitical scienceEconomicsLawMathematicsComputer science

Abstract

fetched live from OpenAlex

ABSTRACT Mexico, Indonesia, Nigeria and Turkey (MINTs) are proclaimed to join the G7 by 2050. Since innovation is strongly associated with competitiveness and high rates of economic growth, we use selected Global Innovation Index (GII) variables to examine the current status and the growth potential of MINTs. R&D appears to be the most significant hurdle for MINTs and is capable of explaining the GII for MINTs at Rsq = .96 by itself. Neither Political Stability nor Rule of Law is encouraging for MINTs. However, University/R&D Collaboration, Intensity of Local Collaboration and Infrastructure, provide MINTs some opportunity in closing the gap with G7. Keywords Global Innovation Index, Political Stability, R&D, Rule of Law, Infrastructure.

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)0.003
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: none
Teacher disagreement score0.012
Threshold uncertainty score0.041

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0060.009
Open science0.0010.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0120.001

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 head0.023
GPT teacher head0.201
Teacher spread0.177 · 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

Citations1
Published2014
Admission routes1
Has abstractyes

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