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Record W1661253376 · doi:10.51358/id.v11i1.243

Design de informação em interfaces digitais: origens, definições e fundamentos

2014· article· pt· W1661253376 on OpenAlexaff
Fernanda Quintão, Ricardo Triska

Bibliographic record

VenueInfoDesign - Revista Brasileira de Design da Informação · 2014
Typearticle
Languagept
FieldComputer Science
TopicInformation Architecture and Usability
Canadian institutionsCégep de l'Outaouais
Fundersnot available
KeywordsComputer sciencePhilosophyHumanitiesSociology

Abstract

fetched live from OpenAlex

Nos dias atuais, o design envolve a produção não só de objetos materiais, mas também de interfaces gráfico-digitais, com as quais o usuário interage no ciberespaço. Existem pontos de contato entre o design de informação e o design de interfaces, uma vez que ambas as disciplinas lidam com informações e signos. São identificados dois marcos para a origem do design de informação, ambos oriundos da década de 1930: o mapa das linhas de metrô de Londres, de autoria de Harry C. Beck, e o trabalho desenvolvido por Otto Neurath, que introduziu o método Isotype. Observa-se que diferentes definições para a disciplina ou campo de estudo destacam a importância do usuário no processo do design de informação. São apresentados seus fundamentos, a partir dos estudos de Bertin, Mijksenaar, Tufte e Redig. Percebe-se a necessidade de se atentar às possíveis estratégias para se reforçar, diferenciar e suportar conteúdos a serem apresentados aos usuários, seja em suporte físico ou digital.

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)0.009
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.048

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.016
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0030.004
Science and technology studies0.0020.014
Scholarly communication0.0150.012
Open science0.0020.005
Research integrity0.0040.003
Insufficient payload (model declined to judge)0.0070.002

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 head0.285
Teacher spread0.230 · 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 designTheoretical or conceptual
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

Citations30
Published2014
Admission routes1
Has abstractyes

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