Bibliographic record
Abstract
ABSTRACT – Current literature on online news sharing presents a range of methods and results, alongside contradictory explanations for the phenomenon. To disentangle this, we compared three countries with contrasting levels of political stability (Brazil, Canada and the US). A content analysis of articles (n = 1.658) posted in 2016, on the main news pages in Facebook for each country, shows that the Canadian news pages presented far less news sharing on national politics conflicts than Brazil and the US did. We discuss how this shows the relevance of political context in explaining both news routines and shareworthiness. RESUMO – A literatura sobre compartilhamento de notícias online apresenta uma gama de métodos e resultados com linhas contraditórias de explicação para o fenômeno. Para destrinchar o problema, comparamos três países com níveis contrastantes de estabilidade política (Brasil, Canadá e EUA). Uma análise de conteúdo de notícias (n = 1.658) postadas nas principais páginas noticiosas do Facebook de cada país em 2016 mostra que as páginas canadenses apresentaram muito menos compartilhamento sobre conflitos políticos nacionais do que nos EUA e Brasil. Discutimos como isso mostra a relevância do contexto político para explicar as rotinas produtivas e a compartilhabilidade. RESUMEN – La literatura actual sobre el intercambio de noticias en línea presenta una variedad de métodos y resultados junto con líneas de explicación contradictorias para el fenómeno. Para desenredar esto, comparamos tres países con niveles contrastantes de estabilidad política (Brasil, Canadá y EE. UU.). Un análisis de contenido de los artículos (n = 1.658) publicados en las principales páginas de noticias en Facebook de cada país en 2016 muestra que las páginas de noticias canadienses presentaron mucho menos intercambio de noticias sobre conflictos políticos nacionales. Lo contrario ocurrió en Brasil y Estados Unidos. Discutimos cómo esto muestra la relevancia del contexto político para explicar tanto las rutinas informativas como las compartidas.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.008 | 0.002 |
| Scholarly communication | 0.003 | 0.000 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".