Infodemia en la Argentina preventivamente aislada. Un análisis de las Fake News sobre la pandemia de COVID-19 desmentidas por Confiar
Bibliographic record
Abstract
El objetivo del presente estudio fue de analizar la difusión de Fake News en el marco de la estrategia de comunicación de riesgo implementada durante la pandemia de COVID-19 por el gobierno en Argentina. La metodología utilizada fue el análisis cualitativo y cuantitativo de las notas publicadas a lo largo del año de 2020 en la plataforma Confiar de la Secretaría de Medios y Comunicación Pública, desarrollada por la Agencia Nacional de Noticias Télam. Los resultados muestran una gran incidencia de contenidos sobre tratamientos no reconocidos por la ciencia, seguidos por teorías de la conspiración; ambas, características flagrantes de una Infodemia. También concluimos que, a pesar del enorme aporte, Confiar dejó algo que desear en algunos aspectos a la hora de manejar la maraña informativa del período.
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 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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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 teacher head, 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".