Most Americans say 'fake news' is confusing
Bibliographic record
Abstract
A new survey from the Pew Research Center has found that two-thirds of U.S. adults say fake news stories are causing "a great deal of confusion" about the basic facts of current events. Most adults in America say that "fake news" from online sources like Facebook is confusing people about actual current events, but a lot of them share it anyway according to a new Pew Research Center survey.The survey found that two-thirds of the people questioned said fake news faked them out.Nearly a third of the people in the Pew survey said they see made-up political news stories online, and they see them often.Less than a half of them said they were "very confident" that they could spot fabricated news.About 45 percent were "somewhat" confident they knew fake news when they saw it. But nearly a quarter of respondents said they shared a fake news story, with fourteen percent shared a story they knew it was fake.Republicans and Democrats were equally likely to say that fake news stories leave Americans "deeply confused."The Pew survey was conducted Dec. 1-4 among 1,002 U.S. adults.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.006 |
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".