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EXPLORANDO A PARCERIA PÚBLICO-PRIVADA EM DEFESA CIVIL: UMA INICIAÇÃO À PESQUISA ATRAVÉS DO PROGRAMA DE EDUCAÇÃO TUTORIAL

2023· article· pt· W4387349940 on OpenAlexaff
MARIA Fernanda Medici Ribeiro, Marianna Dias Presoti Vicentini, Jordan Henrique de Souza, Júlia Righi de Almeida

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldSocial Sciences
TopicBrazilian Legal Issues
Canadian institutionsDiscovery Air (Canada)
FundersUniversidade Federal de Juiz de Fora
KeywordsPolitical scienceHumanitiesArt

Abstract

fetched live from OpenAlex

This article highlights the importance of understanding Public-Private Partnerships for effective civil defense management in the country, through the experience gained from the development of a Research Initiation within the Tutorial Education Program of Civil Engineering at the Federal University of Juiz de Fora. As a result, a didactic material on the subject of Public-Private Partnerships (PPPs) was developed

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient 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 score0.413
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.005
Science and technology studies0.0010.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.007

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 head0.071
GPT teacher head0.373
Teacher spread0.302 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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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