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Record W2950244718 · doi:10.34630/polissema.vi18.3198

CONTRIBUTOS DO TEXTO PARA A TERMINOLOGIA DE BASE CONCEPTUAL. O CONCEITO DE "BLENDED LEARNING": UMA VIAGEM DIACRÓNICA

2019· article· pt· W2950244718 on OpenAlexaff
Joana Fernandes

Bibliographic record

VenueInstituto Politécnico do Porto · 2019
Typearticle
Languagept
FieldArts and Humanities
Topiclinguistics and terminology studies
Canadian institutionsCanadian Linguistic Association
Fundersnot available
KeywordsTerminologyPremiseGRASPObject (grammar)Computer scienceLinguisticsSociology of scientific knowledgeEpistemologyArtificial intelligencePhilosophy

Abstract

fetched live from OpenAlex

Este artigo constituiu um exercício de reflexão sobre um dos cenários educativos mais promissores do Ensino Superior atual: o blended learning. Assumimos o ponto de vista da Terminologia, disciplina que se ocupa da representação, da descrição e da definição do conhecimento especializado através da língua. Neste quadro teórico, os conceitos, enquanto elementos da estrutura do conhecimento, constituem um objeto de investigação de complexidade não despicienda, pois, apesar do postulado de que a língua é uma ferramenta fundamental para descrever e organizar o conhecimento, o princípio isomórfico não pode ser tomado como adquirido. A abordagem conceptual em Terminologia propõe uma visão precisa do papel da língua no trabalho terminológico, sendo premissa basilar que não existe uma correspondência unívoca entre os elementos atomísticos do conhecimento e os elementos da expressão linguística. Não obstante, como será demonstrado, a análise do texto de especialidade é uma ferramenta relevante para acesso ao conceito, constituindo um dos principais ambientes discursivos ao dispor do terminólogo.

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 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)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.947
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0010.003
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.055
GPT teacher head0.300
Teacher spread0.245 · 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; both teacher heads agree on what is shown here.

Study designNot applicable
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
Published2019
Admission routes1
Has abstractyes

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