Fake News: o “espetáculo” como gênese das “notícias falsas”
Bibliographic record
Abstract
En contraste con la comprensión de que las noticias falsas son solo la reversión de la “verdad”, la “verdad” que se ha convertido en mentira, “este artículo propone vincular la noción de “noticias falsas “a los desarrollos contemporáneos del concepto de espectáculo, presentado en 1967 por Guy Debord. Para hacerlo, el argumento se divide en tres puntos principales: la relación entre el espectáculo y la sociedad excitada, según formulado por Christoph Türcke (2010); la reducción del debate político a una política del efecto sujeto a los juegos de “Circulación de sí mismo”; finalmente, la consolidación de lo que Pierre Laval y Christian Dardot (2016) llaman “nueva razón en el mundo”, una racionalidad basada en una forma de subjetivación empresarial y narcisista. Lo que se espera demonstrar, en resumen, es que sin la reflexión sobre estas dimensiones, el debate sobre las noticias falsas se vuelve inocuo.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".