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Record W4280496465 · doi:10.51891/rease.v8i4.5159

ENSINO DA LÍNGUA INGLESA EM TEMPOS DE ENSINO REMOTO

2022· article· pt· W4280496465 on OpenAlexaff
Astrogilda Silva de Oliveira, Carlos Henrique da Silva Santos, Fabiana Angelo, Delze Maria Xavier Bispo Rezende, Lorraine Rossmann Gonçalves

Bibliographic record

VenueRevista Ibero-Americana de Humanidades, Ciências e Educação · 2022
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsImpact
Fundersnot available
KeywordsHumanitiesPhilosophyArt

Abstract

fetched live from OpenAlex

A literatura possui diversas tipificações e abordagens, que se manifestam em obras literárias. A literatura infanto-juvenil ocupa um papel estratégico no ensino de Literatura no ensino fundamental, com obras literárias que possuem linguagem e abordagem de temas condizentes com as demandas de aprendizagem de uma geração de alunos, que se encontram já inseridos na cibercultura. O objetivo deste estudo consiste em discutir sobre os desafios em se desenvolver estratégias de ensino para trabalhar as obras de literatura infanto-juvenil no ensino fundamental, considerando o contexto de cibercultura. Este estudo se caracteriza como uma pesquisa bibliográfica. A relevância deste estudo consiste na importância de se desenvolver o ensino de Literatura no ensino fundamental, por meio de estratégias de ensino e obras literárias que atendam as demandas de aprendizagem dos alunos. As estratégias de ensino de Literatura podem trabalhar as obras literárias infanto-juvenis por meio do diálogo entre o leitor e o texto, utilizando-se de diversas formas de interações disponibilizadas pela cultura impressa e pela cibercultura.

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)0.002
metaresearch head score (Gemma)0.011
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: none
Teacher disagreement score0.022
Threshold uncertainty score0.075

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.011
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0030.004
Science and technology studies0.0050.003
Scholarly communication0.0080.007
Open science0.0010.006
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0220.005

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 head0.062
GPT teacher head0.336
Teacher spread0.274 · 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".

Quick stats

Citations0
Published2022
Admission routes1
Has abstractyes

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