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Explorando a retenção de informações sobre contracepção a partir de diferentes usos da linguagem gráfica

2021· article· pt· W3209517283 on OpenAlexaff
Caroline Winkelmann, Gabriela Botelho Mager

Bibliographic record

Venuenot available
Typearticle
Languagept
FieldSocial Sciences
TopicScience and Education Research
Canadian institutionsEncana (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsPsychologyPhilosophy

Abstract

fetched live from OpenAlex

"Buscando avaliar se, e caso sim, como, diferentes usos da linguagem gráfica podem interferir na retençãoda informação, foi desenvolvido um protocolo usando a análise combinada de dados coletados com usodo equipamento Eye-Tracker SMI e entrevistas semiestruturadas. Com isto, foi possível explorar se aatenção, a compreensão e a percepção de informação sobre contracepção resultavam em diferentesníveis de retenção da informação vista por parte de um público de jovens adultas a depender do tipo delinguagem gráfica usada nas peças gráficas do estudo. Do desenvolvimento, teste, correção e aplicaçãodeste protocolo saíram percepções das possibilidades e desafios na avaliação de algo tão subjetivoquanto a memória e tão delicado quanto a educação sexual, e como estes podem estar relacionados acomo informações são apresentadas em uma peça gráfica estática e digital. Este artigo descreve, por fim,os caminhos traçados no desenvolvimento e aplicação desta pesquisa a fim de partilhar o que foiencontrado e expor indícios relevantes sobre a comunicação de saúde sexual."

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)0.011
metaresearch head score (Gemma)0.033
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.011
Threshold uncertainty score0.056

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.033
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.002
Science and technology studies0.0020.002
Scholarly communication0.0030.003
Open science0.0010.003
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0100.001

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 head0.142
GPT teacher head0.424
Teacher spread0.282 · 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 designObservational
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".

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Citations0
Published2021
Admission routes1
Has abstractyes

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