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Record W4387159737 · doi:10.29397/reciis.v17i3.3351

Revisão por pares das estratégias de busca para revisões sistemáticas: o PRESS, histórico, tradução para o português e funcionalidade

2023· article· pt· W4387159737 on OpenAlexaboutno aff
Daniele Masterson, Martha Sílvia Martinez-Silveira, Cícera Henrique da Silva, Josué Laguardia

Bibliographic record

VenueReciis · 2023
Typearticle
Languagept
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
Fundersnot available
KeywordsHumanitiesPhilosophyPolitical science

Abstract

fetched live from OpenAlex

O Peer Review of Electronic Search Strategies (PRESS) é um instrumento elaborado na Canadian Agency for Drugs & Technologies in Health (CADTH) para avaliar cada elemento das estratégias de busca em bases de dados eletrônicas que podem influenciar a base das evidências das revisões sistemáticas. Os autores obtiveram licença para traduzir o PRESS para o português. O objetivo é contribuir para disseminação, uso e posterior implementação do PRESS, especialmente entre os bibliotecários, consolidando uma prática de avaliação de estratégias de busca das revisões sistemáticas. A metodologia foi o relato de experiência. Para contextualizar, inicia-se com o histórico da construção do PRESS, seguido do processo da tradução e apresentação das funcionalidades de cada tabela. O resultado é a disponibilização da versão do PRESS em português na página da CADTH. Conclui-se que a tradução deve impactar positivamente na qualidade das estratégias de busca das revisões sistemáticas com participação de bibliotecários brasileiros.

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)0.076
metaresearch head score (Gemma)0.230
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.924
Threshold uncertainty score0.400

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0760.230
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0160.024
Science and technology studies0.0080.012
Scholarly communication0.0300.019
Open science0.0030.007
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0190.004

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 head0.125
GPT teacher head0.338
Teacher spread0.213 · 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.

Study designTheoretical or conceptual
DomainMethods
GenreMethods

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".

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Citations0
Published2023
Admission routes1
Has abstractyes

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