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Record W2044439336 · doi:10.5539/ibr.v8n2p51

Intention to Read News on the Internet: A Brazilian Public Analysis

2015· article· en· W2044439336 on OpenAlexvenueno aff
Renato Fonseca Alves de Andrade, Carlos Roberto Camello Lima, Fernando Antônio de Melo Pereira

Bibliographic record

VenueInternational Business Research · 2015
Typearticle
Languageen
FieldSocial Sciences
TopicEducation and Digital Technologies
Canadian institutionsnot available
Fundersnot available
KeywordsThe InternetNewspaperReading (process)Exploratory factor analysisComputer scienceInternet usersLogistic regressionWorld Wide WebAdvertisingPerceptionService (business)PsychologyBusinessPolitical scienceMarketing

Abstract

fetched live from OpenAlex

The main objective of the research was to identify factors related to the usage of the Internet service of readings, assessed by Brazilian readers and evaluate the intended use of readers based on the factors we have found. The methodological procedures involved conducted an online survey research, with the formation of a database formed by the Brazilian public that uses the Internet. Multivariate analysis techniques, such as exploratory factor analysis and logistic regression analysis were used. In the perception of the readers, there are three dimensions that differentiate the innovative user from the late user regarding the use of the web to read news, namely: the practicality of the platform, knowledge of technology and the external influence. From the results it can be concluded that the innovative public drives the latent growth of reading newspapers on the Internet and those who do not intend to use in the future may become users following the evolution and popularization of digital media.

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)0.002
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, 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: none
Teacher disagreement score0.755
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.004
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.349
GPT teacher head0.496
Teacher spread0.148 · 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
Published2015
Admission routes1
Has abstractyes

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