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Record W4232426346 · doi:10.5539/ijef.v13n11p53

Knowledge Economy in Brazil: Analysis of Sectoral Concentration and Production by Region

2021· article· en· W4232426346 on OpenAlexvenueno aff
José Antônio de França, Wilfredo Sosa

Bibliographic record

VenueInternational Journal of Economics and Finance · 2021
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic and Technological Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsTimelineKnowledge productionProduction (economics)Context (archaeology)Unit (ring theory)Index (typography)Public policyRegional scienceEconomicsEconomic geographyGeographyEconomic growthMacroeconomicsMathematicsKnowledge management

Abstract

fetched live from OpenAlex

The research presented in this article investigates and analyzes the concentration of knowledge production in Brazil, in the context of a public policy, at postgraduate level, by using the spectral methods grounded on the LQ (location quotient) and CI (concentration index) indicators, in three dimensions, from 2013 to 2018. The dimensions are economics, geography, and time. Economics is represented by Fields and Major Fields of knowledge production. Geography corresponds to the regions identified by each Federation unit (FU). Time is a chronological unit of the timeline in which knowledge is produced. The research then evaluates knowledge concentration in the income performance of the families by FU. The results are robust and indicate significant evidence that sectorial knowledge production in Brazil is regionally unequal and impacts on family incomes, but those family incomes evolve regardless of the knowledge concentration level produced. The research contributions are relevant to assist public policy regulators and monitoring managers, as well as to encourage future discoveries in regional economics applications.

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 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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.385

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.032
GPT teacher head0.246
Teacher spread0.214 · 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 teacher head, 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
Published2021
Admission routes1
Has abstractyes

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