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Record W3105366756

Tratamento temático da informação em documentos arquivísticos

2016· article· pt· W3105366756 on OpenAlexaboutno aff
Graziela Martins de Medeiros, Luciane Paula Vital, Marisa Bräscher

Bibliographic record

VenueTendências da Pesquisa Brasileira em Ciência da Informação · 2016
Typearticle
Languagept
FieldComputer Science
TopicInformation Science and Libraries
Canadian institutionsnot available
Fundersnot available
KeywordsAnnalsLibrary scienceTheme (computing)Representation (politics)SociologyPolitical scienceHistoryComputer sciencePoliticsWorld Wide WebLaw
DOInot available

Abstract

fetched live from OpenAlex

This article discusses the thematic processing of information, in order to understand the development of this theme in the international archival field. The research was characterized as exploratory and descriptive literature and qualitative approach. The texts were selected in the annals of the International Society of Knowledge Organization (ISKO), which include the International ISKO and national chapters: ISKO-Brazil, ISKO España-Portugal, ISKO-France and NASKO (Canada and United States) and in the annals Working Group (WG) 2 of the National Meeting of Research in Information Science (ENANCIB). 26 articles were evaluated on the following criteria: general aspects (number of items, events, years languages); analysis of authorship (training and professional performance); Text approach; denominations. With the analysis was possible to increase the understanding of the way the thematic representation of information in archival documents has been discussed.

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)0.086
metaresearch head score (Gemma)0.135
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.981
Threshold uncertainty score0.455

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0860.135
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0190.020
Science and technology studies0.0100.024
Scholarly communication0.0190.023
Open science0.0020.009
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0040.001

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 head0.044
GPT teacher head0.283
Teacher spread0.240 · 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.

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
Published2016
Admission routes1
Has abstractyes

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