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Record W2203360083 · doi:10.11606/rai.v12i4.101885

USANDO AS LENTES DA ESTRATÉGIA PARA COMPREENDER OS DETERMINANTES DO DESEMPENHO EM PROJETOS DE PESQUISA E INOVAÇÃO AGROPECUÁRIA

2015· article· pt· W2203360083 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueRAI revista de administração e inovação · 2015
Typearticle
Languagept
FieldDecision Sciences
TopicBusiness and Management Studies
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsHumanitiesPolitical scienceArt

Abstract

fetched live from OpenAlex

O presente trabalho objetiva identificar os fatores que afetam o desempenho de projetos de pesquisa e inovação agropecuária numa instituição pública de pesquisa. Selecionou-se uma amostra de 40 projetos de pesquisa da Embrapa e os resultados apontam que equipes heterogêneas e a formação de parcerias, redes e alianças influenciam positivamente no número de tecnologias geradas pelos projetos de pesquisa. O trabalho apontou um trade-off entre número de publicações científicas dos projetos e o número de tecnologias geradas pelos projetos. O número de artigos científicos gerados está correlacionado com a homogeneidade da equipe e com número de pesquisadores envolvidos nos projetos.

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.

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)0.006
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.451
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0060.005
Meta-epidemiology (narrow)0.0020.002
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.003
Science and technology studies0.0010.001
Scholarly communication0.0060.002
Open science0.0040.002
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.002

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 head0.225
GPT teacher head0.420
Teacher spread0.195 · 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