Learning from learning in deliberative polls : lessons for design of treatments that promote learning, and measurement of their impact
Abstract/Contents
- Abstract
- Low levels of political knowledge are endemic to the mass public, a state of affairs that has remained roughly constant over the last fifty years (Berelson et al., 1954, Converse, 1964; Delli Carpini & Keeter, 1991, 1996; Luskin, 1987; Price 1999). Recent evidence suggests that not only are people uninformed, they are also likely misinformed (Berinsky 2009; Kull et al. 2003; Kuklinski et al. 2000). Furthermore, the policy relevant knowledge that people have tends to be lopsided, under-representing counter-attitudinal information (Jerit & Barabas, 2011). And the information is increasingly asymmetrically distributed across society (Prior 2005, 2007). Considerable debate exists as to what degree that matters. Some argue that citizens often do quite well, even with limited knowledge, by effectively using 'shortcuts' (Lupia, 1994) -- for example, by successfully imputing a politician's sympathies towards Hispanics based on whether or not the politician shucks tamales before eating them (Popkin, 1991). 'Shortcuts', however, are a somewhat misleading term for heuristic processing that can, and empirically does lead people away from, as well as toward, the 'destination' (Kuklinski & Quirk, 2000), with the more knowledgeable led 'correctly' more often than the less knowledgeable (Lau & Redlawsk, 2001). Incorrect appeals to Condorcet's Jury Theorem have led some to believe that "random errors", made by uninformed citizens, cancel in the aggregate, and hence politically inconsequential (Page & Shapiro, 1992). One doesn't need to know a lot about politics to apprehend that errors in political information are rarely random, and sometimes causally correlated by virtue of a common spigot of misinformation (Kull et al., 2003). And while Condorcet's theorem has been shown to be viable under less than perfect conditions, including correlated votes (Ladha, 1992), the claim has always been understandably limited to the population mean, and not subgroup means. Errors, by leaving politicians with a fainter (or incorrect) idea of what particular constituencies want, may still be politically consequential. One may conclude that the 'extenuationist' arguments are weak (Luskin, 2002). Such a conclusion is bolstered by considerable evidence from both simulation and experimentation that corroborates the intuitive theory that 'informed' opinion varies (albeit modestly many a times) from lay opinion (Althaus 1998, Bartels 1996, Lau and Redlawsk 1997, Luskin, Fishkin, & Jowell, 2002). However, both simulations and experimental studies also have their weaknesses, both empirically and normatively. Simulations have been criticized for a socio-deterministic view of vote choice, while experiments purporting to answer roughly what the population would think if it were informed, must prove that learning is "adequate", and not limited to a subset of items or people, and that people use the information they learn 'optimally'. With this larger normative burden in mind, in chapters ahead, I analyze who learns, what, and how much in Deliberative Polls. The first chapter explores how learning varies by type of treatment, and individual. The second chapter builds on the first, describing some consequences of differential learning. The third chapter explores whether people learn arguments in a Deliberative Poll, briefly examining who learns what. In the last chapter, some of the issues around measurement of learning are discussed, and few strategies to address the difficulties explored.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2011 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Sood, Gaurav |
|---|---|
| Associated with | Stanford University, Department of Communication |
| Primary advisor | Fishkin, James S |
| Thesis advisor | Fishkin, James S |
| Thesis advisor | Iyengar, Shanto |
| Thesis advisor | Krosnick, Jon A |
| Thesis advisor | Luskin, Robert C |
| Advisor | Iyengar, Shanto |
| Advisor | Krosnick, Jon A |
| Advisor | Luskin, Robert C |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Gaurav Sood. |
|---|---|
| Note | Submitted to the Department of Communication. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2011. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2011 by Gaurav Sood
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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