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ChatGPT: ameaça ou contribuição à educação?

2023· article· pt· W4389923973 on OpenAlexaff
Marcello Bonfim, Priscilla Manesco, Luciano Santana Pereira, Vania Konell, Elisabeth Penzlien Tafner, Fabrício Ricardo Lazilha, Janes Tomelin

Bibliographic record

VenueApresentações Trabalhos Científicos · 2023
Typearticle
Languagept
FieldSocial Sciences
TopicAcademic Research in Diverse Fields
Canadian institutionsDiscovery Air (Canada)
Fundersnot available
KeywordsHumanitiesPhysicsComputer sciencePsychologyPhilosophy

Abstract

fetched live from OpenAlex

Esta investigação científica aborda a utilização da ferramenta ChatGPT, baseada em inteligência artificial, e seus efeitos imediatos na educação. Visa explorar o potencial da ferramenta como recurso de apoio educacional, desmistificando a ideia de que ela representa uma ameaça ao processo educativo, apresentando possibilidades do seu uso em atividades rotineiras do processo de ensino e aprendizagem e aumentando o engajamento e a motivação dos estudantes, sem perder de vista a importância de educá-los sobre o uso ético e adequado da IA.

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)0.010
metaresearch head score (Gemma)0.048
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.013
Threshold uncertainty score0.053

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.048
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.002
Science and technology studies0.0030.007
Scholarly communication0.0110.011
Open science0.0030.006
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0130.004

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 head0.109
GPT teacher head0.402
Teacher spread0.293 · 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 designNot applicable
Domainnot available
GenreCommentary

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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