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.p041 [[ https://en.wikipedia.org/wiki/Bruno_Latour | Bruno Latour ]] [[ https://monoskop.org/images/c/c1/Latour_Bruno_Pandoras_Hope_Essays_on_the_Reality_of_Science_Studies.pdf | Pandora's Hope: Essays on the Reality of Science 1999 ]] .p041 When mechanisms (machines, theories) are well established, we should still examine inputs .p042 example black box: [[ https://en.wikipedia.org/wiki/Analysis_of_covariance | ANCOVA ]] Analysis of covariance .p043 Bullshit: biased input or obvious problems with output .p047 detecting felons from photos: training photos of felons are scowling, nonfelon photos are attractive .p053 [[ https://en.wikipedia.org/wiki/Eysenck_Personality_Questionnaire | Eysenck Personality Inventory ]] .p064 Pitchers versus glasses - pitcher don't make us drink more, we order pitchers when we intend to drink more .p087 |
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Calling Bullshit
The Art of Skepticism in a Data-Driven World
Carl T. Bergstrom & Jevin D. West . 2020 . BeavLib 149.73 BER
References but no traceable footnotes or citations
- p029 Misinformation: false claims, not intentionally deceptive
- p029 Disinformation: deliberately spread falsehoods
p030 WhatsApp
p031 2017 Facebook admits 126M US users exposed to Russian propaganda
- p032 Firehose strategy disorients audiences. Eschew consistency, exhaust critical thinking
- p032 Purpose: advertising revenue
- p032 Teens in Macedonia earning $5000/mo, "Pope Francis Shocks the World, Endorses Trump for President"
- p034 mid-2017 FCC net neutrality, 21.7M citizen comments, most fraudulent, 500K similar comments sent at the same exact second 2017 July 19 2:57:15 pm EDT, 500K from Russia
- p038 Superfluous details make lies more persuasive
- p039 With nothing to say, bullshit fills space with inconsequentials
- p040 persuasive (exaggerated authority) or evasive (deflect uncomfortable questions) bullshit
- p040 jargon to exclude outsiders
p041 Bruno Latour Pandora's Hope: Essays on the Reality of Science 1999
- p041 When mechanisms (machines, theories) are well established, we should still examine inputs
p042 example black box: ANCOVA Analysis of covariance
- p043 Bullshit: biased input or obvious problems with output
- p047 detecting felons from photos: training photos of felons are scowling, nonfelon photos are attractive
- p064 Pitchers versus glasses - pitcher don't make us drink more, we order pitchers when we intend to drink more
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