Freakonomics: A Rogue Economist Explores the Hidden Side of Everything20074Steven D. Levitt and Stephen J. Dubner. Freakonomics: A Rogue Economist Explores the Hidden Side of Everything.…
By Hervé Mesure. 2007
229 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Information Systems
View Paper: https://www.semanticscholar.org/paper/b47ee9e161e723e8ca1bd4d232c61ffe8713c0e3
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Premkumar Natarajan
- S. Omohundro
- Emily Sheng
- Kai-Wei Chang
- Nanyun Peng
- David M. Quinn
- Michael J. Sandel
- Nate Soares
In Information Systems
- Premkumar Natarajan
- S. Omohundro
- Emily Sheng
- Kai-Wei Chang
- Nanyun Peng
- David M. Quinn
- Michael J. Sandel
Citations in the corpus
- Teachers’ Bias Against the Mathematical Ability of Female, Black, and Hispanic Students (2019, background): “…research on biases (e.g., Bertrand, Chugh, & Mullainathan, 2005), websites with baby names (e.g., baby center.com), and lists of names perceived as “Whitest” and “Blackest” (ABC News, 2006; Levitt & Dubner, 2009) to identify the most popular Hispanic, Black, and White names for boys and girls.”
- Market Reasoning as Moral Reasoning: Why Economists Should Re-engage with Political Philosophy (2013, background): “Economics “simply doesn’t traffi c in morality” (Levitt and Dubner 2006, pp. 11, 46, 190; see also Robbins 1932).”
- Experimental Evidence on Teachers’ Racial Bias in Student Evaluation: The Role of Grading Scales (2020, background): “In the other condition, teachers randomly receive a response in which the writer’s brother’s name is “Connor,” signaling a White author (names taken from list of most racially distinct names; Levitt & Dubner, 2005).”
- The Basic AI Drives (2008, background): “We should embrace this fact of nature and find a way to channel it toward ends which are positive for humanity.”
- Towards Controllable Biases in Language Generation (2020, methodology): “We use 80 names that are equally divided between popular female and male names, and between popular White and Black names (Levitt and Dubner, 2005).”