#impeachment ou #naovaitergolpe: uma análise sobre a folksonomia na indexação de imagens fotográficas em redes sociais da Web 2.0
Bibliographic record
Abstract
Analisa a Folksonomia sob a perspectiva da indexacao de imagens fotograficas em um estudo de caso nas redes sociais Flickr e Instagram. Insere-se no contexto da crise sociopolitica brasileira culminada pelo impeachment da entao presidente Dilma Rousseff em 2016, quando os brasileiros, alem de irem as ruas, foram tambem a Internet e se dividiram em tags contra e a favor do governo. Dentro deste contexto, busca identificar os padroes de etiquetagem utilizados pelos usuarios nas duas redes com base nos niveis de compreensao da imagem de Panofsky (2009) e na Dimensao Expressiva de Manini (2002), em paralelo com os estilos de etiquetagem de Canada (2006), para que, assim, fosse possivel analisar o potencial da etiquetagem como metodo de representacao da informacao imagetica e como fator de construcao da memoria coletiva. Conclui que, apesar das redes apresentarem o mesmo nivel predominante de compreensao da imagem, as motivacoes e os respectivos estilos de etiquetagem divergem e, logo, a precisao na busca e recuperacao de imagens tambem, o que ressalta a necessidade do profissional da informacao conhecer a fundo o comportamento do usuario nas novas plataformas virtuais para que possa continuar a garantir a disponibilidade e acessibilidade a informacao e tambem a propria preservacao da memoria.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.008 | 0.010 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.011 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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.
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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".