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Record W4287510324 · doi:10.56238/sevedi76016-006

Impacto da tecnologia nos adolescentes durante a pandemia

2022· book-chapter· pt· W4287510324 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

VenueSeven Editora eBooks · 2022
Typebook-chapter
Languagept
FieldSocial Sciences
TopicEducation during COVID-19 pandemic
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesCoronavirus disease 2019 (COVID-19)PsychologyArtMedicine

Abstract

fetched live from OpenAlex

Este trabalho analisa o comportamento digital e educacional de estudantes adolescentes, durante o período de isolamento social, provocado pela pandemia da Covid-19 em 2020. Trata-se de uma pesquisa de natureza quantitativa, a partir de uma amostra de 272 participantes de Mato Grosso. Os resultados apontam que a) os adolescentes percebem os excessos na utilização de ferramentas digitais para as práticas de interação interpessoal, potencializadas pela pandemia; b) apesar do tempo disponível, durante o isolamento social, preferiram atividades de entretenimento mais passivas, como séries, filmes e jogos virtuais, com intensa desmotivação para realizar projetos artísticos e atividades intelectuais, mais exigentes, como leitura e estudos escolares. Portanto, mesmo diante de inúmeras ferramentas digitais e tecnológicas disponíveis para a aprendizagem e o entretenimento, os processos criativos e artísticos exigem elementos motivacionais exteriores que promovam a ação intelectual.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Research integrity, Insufficient payload (model declined to judge)
Consensus categoriesResearch integrity, Insufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.475
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.000
Science and technology studies0.0020.001
Scholarly communication0.0000.000
Open science0.0020.001
Research integrity0.0020.004
Insufficient payload (model declined to judge)0.0070.001

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 head0.048
GPT teacher head0.330
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