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Technology Transfer Process in Brazil

2023· book-chapter· en· W4381145118 on OpenAlexaboutno aff
Luan Carlos Santos Silva, Carla Schwengber ten Caten

Bibliographic record

VenueAdvances in human and social aspects of technology book series · 2023
Typebook-chapter
Languageen
FieldEconomics, Econometrics and Finance
TopicEconomic Growth and Productivity
Canadian institutionsnot available
Fundersnot available
KeywordsTechnology transferBusinessProcess (computing)Industrial organizationEconomic growthInternational tradeEconomicsComputer science

Abstract

fetched live from OpenAlex

The objective of this study is to analyze the technology transfer flow in Brazil from 2000 to 2014, considering the domestic and foreign markets, and also the activities carried out by universities and technological institutes from 1972 to 2015. From results, Brazil has been receiving technologies from major economic powers such as the United States, Germany, Japan, France, Italy, the United Kingdom, Switzerland, Canada, and Spain. The barriers regarding cooperation between university-industry are still present, currently there are 27.523 research groups distributed in all areas of knowledge, but only 0.31% develops activities related to technology transfer, and 58% do not establish any relationships with industry. Notwithstanding, the technology supply must depend on the technological diffusion process and the adoption of technology by the society through continuous learning, thereby enabling to increase the performance of services, processes, and products in the domestic market.

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)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.042
Threshold uncertainty score0.083

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0040.005
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0000.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0050.001

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 head0.015
GPT teacher head0.239
Teacher spread0.224 · 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 designQualitative
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
Published2023
Admission routes1
Has abstractyes

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