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Record W6957892924 · doi:10.60692/rhw8r-1gg42

TikTok como ferramenta de inovação em serviços de informação em bibliotecas

2022· article· pt· W6957892924 on OpenAlexaboutno aff

Bibliographic record

VenueGreater South Information System · 2022
Typearticle
Languagept
FieldComputer Science
TopicHealthcare during COVID-19 Pandemic
Canadian institutionsnot available
Fundersnot available
KeywordsField (mathematics)Order (exchange)Digital libraryInstitutionSubject (documents)

Abstract

fetched live from OpenAlex

O estudo tem como objetivo analisar o posicionamento digital de algumas bibliotecas na rede social TikTok, observando o uso dessa plataforma como estratégia de inovação em serviços na perspectiva das novas tendências de consumo da informação. Trata-se de uma pesquisa descritiva, de abordagem qualitativa, cujo delineamento do estudo foi dividido em duas fases: pesquisa bibliográfica (levantamento de pesquisas e relatos de experiências sobre o tema) e levantamento dos perfis de bibliotecas na rede social que utilizam a plataforma. Foram analisados cinco perfis: Biblioteca Pública Sierra Madre, Biblioteca Pública de Vancouver, Biblioteca Pública de Iowa, Biblioteca Pública de Dover e a Biblioteca Pública de Calgary. O processo de análise da pesquisa sobre o posicionamento digital desses espaços foi realizado a partir de quatro aspectos: marketing, engajamento, interação com os usuários e produção de conteúdo. Observou-se que essas bibliotecas, aliando seu caráter profissional ao potencial de entretenimento da rede social TikTok, desenvolveram uma relação de interação e de engajamento com os seus usuários, o que gerou maior interesse pelos seus serviços de informação e produtos por meio do marketing digital. Tornaram-se, assim, inovadoras em seu posicionamento digital estratégico, acompanhando as novas tendências de consumo da informação pelos usuários.

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)0.004
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.674
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0040.005
Science and technology studies0.0020.000
Scholarly communication0.0020.003
Open science0.0030.002
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.073
GPT teacher head0.279
Teacher spread0.205 · 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 teacher head, not a consensus.

Study designQualitative
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
Published2022
Admission routes1
Has abstractyes

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