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Record W3198467160 · doi:10.29173/irie397

Competência em Informação no papel social do tutor a distância

2021· article· pt· W3198467160 on OpenAlexvenueno aff
Jéssica Gabriela Tamião de Souza, Luciane de Fátima Beckman Cavalcante

Bibliographic record

VenueThe International Review of Information Ethics · 2021
Typearticle
Languagept
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsTUTORSociologyPedagogyHumanitiesPsychologyPhilosophy

Abstract

fetched live from OpenAlex

O presente artigo analisa a Competência em Informação (CoInfo) no contexto da Educação a Distância (EAD), por meio da prática profissional do tutor a distância. Reflete sobre os aspectos sociais da prática do profissional tutor, embasado teoricamente nos sete pilares de Bruce (1999), sobretudo em seu último pilar que aborda a utilização da CoInfo em benefício do próximo. Os dados foram coletados por meio de pesquisa semiestruturada com tutores a distância, em uma instituição de ensino, de uma cidade do interior do Estado do Paraná. Os resultados evidenciam que, embora os tutores apresentem habilidades informacionais, as práticsa da CoInfo e, sobretudo, do pensamento crítico não são institucionalizados em seu local de trabalho.

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)0.017
metaresearch head score (Gemma)0.064
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: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score0.089

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0170.064
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.006
Science and technology studies0.0040.010
Scholarly communication0.0160.009
Open science0.0010.010
Research integrity0.0030.002
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 head0.083
GPT teacher head0.406
Teacher spread0.323 · 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
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
Published2021
Admission routes1
Has abstractyes

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