Fake News: Combata esse vírus! Projeto Fake Não!
Bibliographic record
Abstract
A COVID-19, doença causada pelo vírus SARS-CoV-2, tem como característica a rápida transmissão. Desde seu relato no final de dezembro de 2019, em Wuhan, na China, a doença se alastrou pelos 5 continentes. Em 20 março de 2020, o governador de Alagoas decretou estado de calamidade pública no Estado de Alagoas atribuído à pandemia do COVID-19, suspendendo o funcionamento de vários estabelecimentos. Face ao exposto, a quantidade de conteúdos veiculados nas redes sociais acerca da pandemia contribui com a disseminação de notícias falsas. Essas notícias, uma vez espalhadas, dificultam a adesão às medidas de contenção do vírus orientadas pelas agências oficiais de saúde. Uma das consequências é o aumento do número de pessoas infectadas, levando à sobrecarga do sistema de saúde. Dessa maneira, o presente projeto busca minimizar os impactos da propagação de informações falsas a partir de debates das Fake News relacionadas à COVID-19. Neste boletim, vamos apresentar sete notícias falsas relacionadas com a COVID-19.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.071 | 0.040 |
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".