---
title: "[WEEK 4 TITLE]"
subtitle: "[WEEK 4 SUBTITLE]"
date: last-modified
date-format: "[Updated ]MMM D, YYYY"
format: 
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```{r}
#| label: init
#| echo: false
#| results: hide
#| warning: false 
#| message: false

library(tidyverse)
library(labelled)
library(haven)
library(DeclareDesign)
library(easystats)
library(texreg)
library(DT)

```



# {{< fa map-location>}} Tuesday {.inverse}

## Overview {.smaller}

[Monday:]{.blue}

- Groups assigned for Group Project

- Recap discussion of Ideology and Issues

- General overview of political knowledge


[Thursday:]{.blue}

- Finish Jerit, Barabas and Bolsen (2006)

- Alternative conceptions of political knowledge Weaver, Prowse and Piston (2019)

- Misinformation (Jerit and Zhao 2020)

## Readings for Political Cognition

By Thursday:

- @Zaller1992-mz

- @Lodge2013-kq



## Group Projects

```{r}
#| echo: false
groups_df <- read_csv("data/POLS 1140 Survey.csv")


groups_df %>%
  group_by(name) %>% 
  mutate(
    Name = str_split(name, ", ")[[1]][2],
  ) %>% 
  ungroup() %>% 
  mutate(
    Group = paste("Group", group_id)
  ) %>% 
  group_by(Group) %>% 
  summarise(
    Members = paste(Name, collapse = ", ")
  ) -> group_tab
  
DT::datatable(group_tab,escape = F)


```


## Assignment 1:

- Today:
  - Get group contact info
  - Brainstorm and share ideas
- By Next Tuesday:
  - [1 Page Memo:](https://docs.google.com/document/d/1VnaAFMyZZ_Vv5h_V8S6sYQTHMbo7-GE5xyoHOBPoIOY/edit#heading=h.4gb99tjfeten)
  - General topic
  - Elevator pitch
  - Outcomes of interest
  - Key predictors



# Review

## Review

What have we learned so far?

- Converse (1964)

- Ansolabehere, Rodden, and Snyder (2008)

- Freeder, Lenz and Turney (2019)


## Converse (1964) {.smaller}

- Most citizens lack what stable, coherent ideological belief systems

- Shallow understanding of liberal-conservative labels

- Inconsistent beliefs across issue, particular among the mass public

- Centrality of groups and possiblity of issue publics

## Ansolabehere, Rodden, and Snyder (2008) {.smaller}

- A critique of Converse (1964) suggesting the apparent instabiltiy and incoherence of issue attitudes is a product of measurement error

- Using multiple items to measure latent concepts:

  - Reduces measurement error
  - Increases stability and predictive validity of measures

## Freeder, Lenz, and Turney (2019) {.smaller}

- A critique of Ansolabehere's critique, suggesting that simply constructing multi-item scales reduces multiple sources of error and instability, only some of which is classic measurement error

- Returns to Converse's concept of "what goes with what," using candidate and party placements (how) as a proxy for this form of knowledge.

- Show that knowledge of what goes with what is the driving force of ideological consistency and stability

  - But only for folks that agree with their party/candidate's position.


## First Reflection Papers Due February 24

- Due February 24th

- May be on any topic we've covered so far

  - Ideology
  - Political Knowledge
  - Political Cognition
  - Democratic Choice
  
## Additional Readings

## Some additional suggested papers

- [Mondak (1983)](https://link.springer.com/article/10.1007/BF00993852)"Public opinion and heuristic processing of source cues"
- [Kam (2005)](https://link.springer.com/article/10.1007/s11109-005-1764-y) "Who Toes the Party Line? Cues, Values, and Individual Differences"
- [Taber and Lodge (2006)](https://onlinelibrary.wiley.com/doi/full/10.1111/j.1540-5907.2006.00214.x) "Motivated Skepticism in the Evaluation of Political Beliefs"
- [Bartels (2005)](https://www.cambridge.org/core/journals/perspectives-on-politics/article/homer-gets-a-tax-cut-inequality-and-public-policy-in-the-american-mind/E57F206B695C64CAF743305E7082AEDC)"Homer Gets a Tax Cut: Inequality and Public Policy in the American Mind"
- [Fowler and Hall (2018)](https://www.journals.uchicago.edu/doi/pdfplus/10.1086/699244) "Do Shark Attacks Influence Presidential Elections? Reassessing a Prominent Finding on Voter Competence"
- [Busby et al (2016)](https://www.journals.uchicago.edu/doi/pdfplus/10.1086/688585) "The Political Relevance of Irrelevant Events"
- [Miller et al. (2016)](https://onlinelibrary.wiley.com/doi/full/10.1111/ajps.12234) "Conspiracy Endorsement as Motivated Reasoning: The Moderating Roles of Political Knowledge and Trust"

## Concepts you should be applying

- Measures of Association
- Measures of Uncertainty
- Models of Causation


## Questions you should be asking {.smaller}

- What's the research question
- What's the theoretical framework
- What's the empirical design
- What are the results
- What are the conclusions

## What's the research question

- What is this paper about?

- If you had to summarize this paper in one to two sentences, how would you do it?

## What's the theoretical framework

- What's the argument?

- What are the key concepts?

- Why do we care? 

- What's the contribution?

- What are the expectations?

## What's the empirical design

- How will the paper convince you of its claims

- Is it an experimental or observational design

- What data does the paper use

- What methods the paper apply


## What are the results

- What evidence does the paper provide to support its claims

- What specific tables and figures support the paper's claims

:::{.callout-note}
In your papers, I'm particularly interested in your ability to take a theoretical claim and map it onto an empirical result. Give me page numbers, tables, estimates. 
:::

## What are the conclusions
    
- What have we learned? 

- What do we still need to know?

- What would we do differently?

## Instagram or TikTok? {.smaller}

```{r}
#| label: week3
#| echo: false

class_df <- haven::read_sav("surveys/wk04.sav")

class_df %>% 
  filter(share == 1) -> class_df

class_df %>% 
  mutate(
    `Brain Rot` = fct_rev(fct_inorder(as_factor(social)))
  ) %>% 
    ggplot(aes(`Brain Rot`, fill=`Brain Rot`))+
  geom_bar()+
  geom_text(stat='count', aes(label=..count..), hjust=-0.25)+
  coord_flip()+
  ylim(0,30)+
  scale_fill_bluebrown()+
  theme_minimal()+
  labs(
    y="Count",
    x="",
    fill = "Brain Rot",
    title = "For whom do I provide free consumer research?"
  )




```

## Chat am I cooked?

```{r}
#| echo: false

class_df %>%
  mutate(
    `Brain Rot` = as_factor(social),
    `Why?`= social_why
  )%>%
  select(`Brain Rot`, `Why?`)%>%
  DT::datatable(
    options = list(
              "pageLength" = 5)
  )
```








# {{< fa lightbulb >}} Political Knowledge {.inverse}

## What should citizens know about politics?

Take a few moments to write down your thoughts:



- What kinds of knowledge do citizens in a democracy need to possess



- Do citizens generally possess this knowledge?


- Is this a problem?

## Luskin (1987)

- [Luskin](https://www.jstor.org/stable/2111227) offers a definition of "political belief systems" defined in terms of
    - size (number of cognitions)
    - range (diversity)
    - organization (constraint)
- Converse's focus on constraint, Delli Carpini and Keeter (1993) argue "putting the cart before the dead horse."

## How Should We Define Political Knowledge

- [Delli Carpini and Keeter (1996)](https://bruknow.library.brown.edu/discovery/openurl?institution=01BU_INST&vid=01BU_INST:BROWN&isbn=9780300062564&genre=book&eisbn=9780300194319&title=What%20Americans%20Know%20about%20Politics%20and%20Why%20It%20Matters&sid=jstor:jstor) define political knowledge as: "the range of factual information about politics that is stored in long-term memory."


## How Should We Measure Political Knowledge

- Open-ended questions can be problematic
    - [Gibson and Caldeira (2009)](https://www.journals.uchicago.edu/doi/abs/10.1017/S0022381609090379)
- Instead, scholars of developed set of questions which we use to measure this concept

## What do you know! {.smaller}


- Do you happen to know what job or office is now held by J.D. Vance? 

- Whose responsibility is it to determine if a law is constitutional or not ... is it the president, the Congress, or the Supreme Court? 


- How much of a majority is required for the US Senate and House to override a presidential veto? 



- Do you happen to know which party has the most members in the House of Representatives in Washington? 


- Would you say that one of the parties is more conservative than the other at the national level? Which party is more conservative? 


## What do you know! {.smaller}

:::{.nonincremental}

- Do you happen to know what job or office is now held by J.D. Vance? **Vice President**

- Whose responsibility is it to determine if a law is constitutional or not ... is it the president, the Congress, or the Supreme Court? **Supreme Court**


- How much of a majority is required for the US Senate and House to override a presidential veto? **Two-thirds**

- Do you happen to know which party has the most members in the House of Representatives in Washington? **Republicans**

- Would you say that one of the parties is more conservative than the other at the national level? Which party is more conservative? **Republican Party**

:::

## What does the public know?

![](https://www.pewresearch.org/wp-content/uploads/sites/4/2018/04/10_6.png)

[Pew](https://www.pewresearch.org/politics/2018/04/26/10-political-engagement-knowledge-and-the-midterms/)

## Annenberg Civics Knowledge Survey

[![](images/04_annenberg.png)](https://www.annenbergpublicpolicycenter.org/political-communication/civics-knowledge-survey/)


## Can you name all three branches of government?

:::{.panel-tabset}

## 2022

![](https://www.annenbergpublicpolicycenter.org/wp-content/uploads/civics-dataviz-piechart-2022-1400x1068.png)


## 2024

![](https://www.annenbergpublicpolicycenter.org/wp-content/uploads/Three_Branches1024_1.jpg)

## 2025

![](https://www.annenbergpublicpolicycenter.org/wp-content/uploads/Figure5_Branches-1140x1368.png)


:::
  

## What Makes a Good Measure

- These standard measures of political knowledge have some nice properties
  - They "scale" together well
  - They discriminate levels of political knowledge

## What Makes a Good Measure? Mix Difficulties

![](images/04_know/dck1.png)


[Delli Carpini and Keeter (1993)](https://www.jstor.org/stable/2111549)

## What Makes a Good Measure? High "Discrimination"

![](images/04_know/dck2.png)


[Delli Carpini and Keeter (1993)](https://www.jstor.org/stable/2111549)

## What Makes a Good Measure

- These standard measures of political knowledge have some nice properties
    - They "scale" together well
    - They discriminate levels of "political knowledge
    - They predict attitudes and behavior
- But what are these scales really measuring?
    - Why would we expect an increase in this measure of PK to increase voting?

## Lupia (2016)

::::{.columns}
:::{.column width="50%"}
- Offers an an important distinction between:
    - Information
    - Knowledge
    - Competence


:::
:::{.column width="50%"}
![](https://global.oup.com/academic/covers/pdp/9780190263720)

:::
::::

## Lupia (2016) {.smaller}

- Information: "Information is what educators can convey to others"



- Knowledge is memories of how concepts and objects are related to each other. 

  - Knowledge requires Information
  


- Competence is the ability to perform a task in a particular way
  
- Competence requires knowledge, but [not complete knowledge.]{.blue} Depends on the context/decision
  - Cues and heuristics can help
  - Competence is contextual and political

## What is necessary and sufficient to make competent decisions?

![](images/04_know/l1.png)

# {{< fa lightbulb >}} Political Knowledge in the 2020 ANES {.inverse}

## Political Knowledge

:::{.panel-tabset}

## Overview

Let's take a look at political knowledge in the [2020 American National Election Study](https://electionstudies.org/data-center/2020-time-series-study/) as measured by four items:

- Length of Senate Term
- Government spending on Foreign Aid
- Party control of House
- Party control of Senate

## {{<fa code>}}

```{r}
#| label: nescode

## ---- Libraries and data ----
library(anesr)
library(tidyverse)
library(labelled)
library(haven)
library(scales)
library(estimatr)
data("timeseries_2020")
df <- timeseries_2020
```

```{r}
#| label: nesrecode
## ---- Recode data ----
df %>% 
  mutate(
    pk_senate_term = case_when(
      V201644 == 6 ~ 1,
      V201644 <  0 ~ NA,
      T ~ 0

    ), 
    pk_foreign_aid = case_when(
      V201645 == 1 ~ 1,
      V201645 <  0 ~ NA,
      T ~ 0

    ),
    pk_house = case_when(
      V201646 == 1 ~ 1,
      V201646 <  0 ~ NA,
      T ~ 0

    ), 
    pk_senate = case_when(
      V201647 == 2 ~ 1,
      V201647 <  0 ~ NA,
      T ~ 0

    ),
    sex = ifelse(V201600 == 2, "Female", "Male"),
    college_degree = ifelse(V201511x > 3, "College Degree", "No College"),
    race = case_when(
      V201549x == 1 ~ "White",
      V201549x == 2 ~ "Black",
      V201549x == 3 ~ "Hispanic",
      V201549x == 4 ~ "Asian",
      T ~ "Other"
    ),
    race = forcats::fct_infreq(race),
    income = case_when(
      V201617x < 0 ~ NA,
      T ~ V201617x
    ),
    political_interest = ifelse(
      V201005 < 0, NA, (V201005 - 5)*-1
    )
  ) %>% 
  mutate(
    political_knowledge = rowSums(
      select(.,starts_with("pk")),
      na.rm=T
    )
    )-> df
```

```{r}
#| label: nesfigures

## ---- Figures ----

## Individual items

df %>% 
  summarise(
    `Senate Term` = mean(pk_senate_term,na.rm=T),
    `Foreign Aid` = mean(pk_foreign_aid, na.rm = T),
    `Dem House` = mean(pk_house, na.rm=T),
    `Rep Senate` = mean(pk_senate, na.rm = T)
  ) %>% 
  pivot_longer(
    cols = 1:4,
    names_to = "Item"
  ) %>% 
  mutate(
    Item = forcats::fct_reorder(Item,value),
    `Percent Correct` = round(value*100,2)
  ) %>% 
  ggplot(aes(Item, `Percent Correct`))+
  geom_bar(stat = "identity")+
  coord_flip()+
  geom_text(aes(label = scales::percent(value)),
            hjust = -.25)+
  ylim(0,100)+
  labs(
    title="Individual Knowledge Items",
    x = ""
  ) +
  theme_minimal()-> fig1

## Knowledge Scale

df %>% 
  group_by(political_knowledge) %>% 
  summarise(
    n = n(),
    prop = n()/nrow(df),
    Percent = scales::percent(prop)
  ) %>% 
  ggplot(aes(political_knowledge, prop))+
  geom_bar(stat = "identity")+
  scale_y_continuous(labels = label_percent())+
  geom_text(aes(y = prop, x= political_knowledge,label = Percent), vjust = -.5)+
  ylim(0, .33)+
  labs(
    x = "Number of Items Correct",
    title = "Political Knowledge Scale"
  ) +
  theme_minimal() -> fig2

# ---- Knowledge Gaps ----

# Education

df %>% 
  ggplot(aes(college_degree, political_knowledge,
             col = college_degree))+
  stat_summary()+
  guides(col = "none")+
  theme_minimal()+
  labs(
    x= "",
    y = "Average Political Knowledge",
    title = "Education"
  ) -> fig3

# Sex

df %>% 
  ggplot(aes(sex, political_knowledge,
             col = sex))+
  stat_summary()+
  guides(col = "none")+
  theme_minimal()+
  labs(
    x= "",
    y = "Average Political Knowledge",
    title = "Sex"
  ) -> fig4

# Race

df %>% 
  ggplot(aes(race, political_knowledge,
             col = race))+
  stat_summary()+
  guides(col = "none")+
  theme_minimal()+
  labs(
    x= "",
    y = "Average Political Knowledge",
    title = "Race"
  ) -> fig5

# Income

df %>% 
  ggplot(aes(income, political_knowledge,
             ))+
  stat_summary(size=.2)+
  theme_minimal()+
  labs(
    x= "",
    y = "Average Political Knowledge",
    title = "Income"
  ) -> fig6
```

```{r}
#| label: models


# ---- Models ----

## Bivariate

m1 <- lm_robust(political_knowledge ~ college_degree, df)
m2 <- lm_robust(political_knowledge ~ sex, df)
m3 <- lm_robust(political_knowledge ~ race, df)
m4 <- lm_robust(political_knowledge ~ income, df)

## Multiple Regression

m5 <- lm_robust(political_knowledge ~ 
                  college_degree + sex + race + income + political_interest , df)



```

## Items

```{r}
#| echo: false
#| label: fig1

fig1
```

## Scale

```{r}
#| echo: false
#| label: fig2

fig2
```

:::

## Knowledge Gaps

:::{.panel-tabset}

## Overview

:::{.nonincremental}

Now let's explore how levels of political knowledge vary by:

- Education
- Sex
- Race
- Income

Take a moment to jot down your expectations
:::

## Education

```{r}
#| echo: false
#| label: fig3

fig3
```

## Sex

```{r}
#| echo: false
#| label: fig4

fig4
```

## Race

```{r}
#| echo: false
#| label: fig5

fig5
```

## Income

```{r}
#| echo: false
#| label: fig6

fig6
```


:::

## Knowledge Gaps{.smaller}

:::{.panel-tabset}

## Overview

We could also explore knowledge gaps using regression. Recall:

- Regression is tool for estimating conditional means

- Regression partitions variance explained by specific factors

## Table
```{r}
#| label: tab1
#| echo: false
#| results: asis

htmlreg(list(m1,m2,m3,m4,m5),
        include.ci = FALSE,
        custom.coef.names = c(
          "(Intercept)",
          "No BA",
          "Male",
          "Hispanic",
          "Black",
          "Other",
          "Asian",
          "Income",
          "Political Interest"
        ))
```

:::


# {{< fa lightbulb >}} Jerit, Barabas, and Bolsen (2006) {.inverse}



## What's the research question

- What's the point of this study?

- Think, pair, share...

## What's the research question

- Jerit et al. argue that political knowledge is not a static concept, but instead varies over time as a function of the characteristics of individuals and their environments

## What's the theoretical framework

- What debate do the authors speak to?
- What are their theoretical contributions to this debate?
- What are the specific expectations from this theory

## What's the theoretical framework

- Jerit et al. address a literature focused on knowledge gaps which:
    - highlights the importance of relatively stable individual factors like education, income, gender, race in predicting differences in knowledge
    - Concludes that any changes in political knowledge are likely to benefit the "informationaly rich"

## What's the theoretical framework

- Jerit et al. lay the foundation for a distinction made by Barabas et al. (2014)
between the type and temporal dimensions of knowledge

![](images/04_know/b1.png)


## What's the theoretical framework

- Jerit et al. argue political knowledge will vary as both a function of characteristics of the individual (education) and the information environments in which they exist

## What's the theoretical framework {.smaller}

From page 268:

- H1a: Increases in the overall amount of media attention to an issue will increase the average amount of knowledge in the population 
- H1b: The gap in knowledge between individuals with low and high levels of education also will increase
- H2a: All else held constant, increasing the amount of newspaper coverage will raise the average level of knowledge in the population, but it should primarily benefit those with high levels of education 
- H2b: An increase in television coverage will raise the average level of knowledge in the population, but it will not alter the relationship between education and knowledge

## What's the empirical design

- What data and methods do the authors use?

## What's the empirical design

-  Measure specific surveillance knowledge from surveys on 41 issues (primarily health related), paired with content analysis of issue-specific coverage in the AP, Broadcast TV News, and USA Today.   
- Study 1 looks at variation in coverage across two issues using a probit regression
- Study 2 looks at variation in coverage and individual education across multiple issues using multilevel modeling

## 😉 What you need to know (WYNK)

- "Dichotomous" taking 1 of two values (1 if correct, 0 otherwise)
- "We ran a multivariate probit..." They did a special type of regression for data that has a binary (0 or 1) outcome
- "A Multilevel Model" They pool data together across surveys and model variation at the level of both the individual $i$ and the issue $j$
- "The grand mean" The mean for the entire sample
- "ANOVA" Analysis of Variance: How much variation is at the individual vs issue level

## What's are the results

- What are the hypotheses?
- What figures and tables provide evidence in support of each claim?

## What are the results

- H1a
    - Text page 271, Figure 2, Figure 3, Table 1, Figure 4   
- H1b
    - Figure 2, Figure 3, Table 1, Figure 4
- H2a
    - Table 1, Figure 4
- H2b
    - Table 1, Figure 4 

## Increasing Media Coverage Increases Knowledge (H1a)

![](images/04_know/j2.png)


## Knowledge Gaps Increase When There is More Coverage (H1b)

![](images/04_know/j3.png)



## Is this Always True?

## Is this Always True?

![](images/04_know/j0.png)



## Increasing Media Coverage Increases Knowledge (H1a)

![](images/04_know/j4.png)


## Knowledge Gaps Increase with more Media Coverage (H1b)

![](images/04_know/j5.png)


## Newspaper Coverage Benefits the Informationally Rich (H2a)

:::{.full}
![](images/04_know/j1.png){width=100%}
:::

## Newspaper Coverage Benefits the Informationally Rich (H2a)

![](images/04_know/j1_c2.png)


## Television Coverage Offers Wider Benefits (H2b)

![](images/04_know/j1_c1.png)


## Media Coverage and Knowledge Gaps 

![](images/04_know/j6.png)



## What's are the conclusions

- What are some potential concerns about this study?
- What are the broader conclusions


# {{<fa lightbulb >}} Weaver, Prowse, and Piston (2019){.inverse}

## Take a few moments

- What's the research question
- What's the theoretical framework
- What's the empirical design
- What are the results
- What are the conclusions

## What's the research question

Prevailing accounts of citizen competence suggest citizens have:

- Too little knowledge $\to$ too much power

WPP invert this claim, contending marginalized groups often have:

- Too much knowledge $\to$ Too little power



## What's the theoretical framework

- WPP intervene in the political knowledge literature, which:

  - Finds citizens possess low levels of factual knowledge

  - Argues this ignorance may undermine democratic decision-making

  - In some cases, suggests citizens exert too much influence (Quirk & Hinchliffe 1998)

## What's the theoretical framework

::::{.columns}
:::{.column width="50%"}

- Following Cramer and Toff (2017) they think of political knowledge in terms of people's lived experiences

- Civic competence depends less on factual recall and more on how people interpret and deploy political experience

:::
:::{.column width="50%"}

![](images/04_images/04_cramer.png)

:::
::::

## What's the theoretical framework

- WPP's work builds on theories of [policy feedback]{.blue}

  - Policy Feedback examines the way policy experiences shape citizens' attitudes and behaviors  
  
- WPP in particular, focus on the [second face]{.blue} of government that deals with aspects of [social control]{.blue}

- WPP argue that citizens in highly policed communities possess extensive knowledge of the state’s coercive face — and that this knowledge reflects democratic failure rather than democratic competence.

## What's the empirical design

![](images/04_know/w1.png)
- 233 conversations between Black residents across 13 highly policed neighborhoods

- Interpretive, inductive analysis of dialogue

## Conversations with Strangers

> The Portal does two things simultaneously, providing the opportunity and space to discuss a topic and connecting them to those who can be assumed to be knowledgeable about that topic.


## Yes, but how representative are these portals? {.smaller}



> Portals participants are not a strictly random sample and we cannot say how representative they are of communities of interest.


> We are after richer data that reveals not just a snapshot of opinion that is “representative,” but how people reason together, how they frame things in their own words not those of the survey researcher, and how they develop a theory of state action and power.



> Second, we would be more concerned about representativeness or bias if we were testing hypotheses about the distributions of attitudes (how many) or causal relationships between variables (how related), studies based on a “sampling logic.


> Finally, existing large-N surveys are notoriously inadequate at capturing the experiences of highly policed communities.

## Summary: Concerns about representativeness {.smaller}

- Not designed to estimate population distributions

- Designed to understand reasoning processes

- Survey methods under-capture highly policed communities

- Goal: depth, not representativeness

## What are the results


Take a few momements to share with your neighbor, your understanding of the study's findings


## Two Much Knowledge, Too Little Power {.smaller}

The crux of their argument follows from their summary of the initial portal conversation (pp. 1155-1158) which they argue reveals:

- Knowledge rooted in repeated, often involuntary encounters with police
- Extensive factual knowledge (Names of public officials, scandals)
- Knowledge of "unwritten rules"
- Multiple sources of information (personal experience, vicarious experience, media coverage)
- Official stories vs counter narratives (Overt curriculum” vs hidden curriculum; Official law vs lived law)


## What are the results {.smaller}

- Pariticipants exhibit Dual Knowledge:

	- What the state claims to do

	- What the state actually does

- Knowledge is:

	-  Deep, specific, experiential

	- Often acquired involuntarily

	- Reinforced by community and media

- Knowledge functions as:

	- A strategy of self-preservation

	- A way to navigate risk

	- A tool for distancing from state oversight

- Conclusion:

	- Residents have “too much knowledge, too little power”  



## Broader Implications

- What counts as political knowledge?

- Should experiential knowledge be treated as epistemically privileged?

- Does this redefine democratic competence?

- What does this imply about measuring political knowledge with surveys?

- When does this knowledge lead to withdrawal rather than participation?

# {{<fa lightbulb>}} Attendance Survey {.inverse}

## Please complete the following survey

[Click here to take the attendance survey](https://brown.co1.qualtrics.com/jfe/form/SV_4NF1fgduxGFOR38)

# {{<fa lightbulb>}} Misinformation {.inverse}

## Motivating questions

- What is misinformation?
- Why do people become misinformed?
- Can we correct misinformation?
- Are reported misperceptions sincere?

## What is misinformation{.smaller}

:::{.panel-tabset}

## Misinformation

-  @Kuklinski2000-id: "People are misinformed when they confidently hold wrong beliefs"

## Misinformation, Rumors, Conspiracy Beliefs


[Rumors]{.blue}

- Lack evidentiary standards

- May turn out to be true

[Conspiracy beliefs]{.blue}

- Explain events via hidden, powerful actors

- Often tied to dispositional predispositions

[Misinformation]{.blue}

- Unambiguously false 

- Confidently held




## Kuklinski et al. (2000)

![](images/04_kuklinski_1.png)

[@Kuklinski2000-id](https://www.journals.uchicago.edu/doi/10.1111/0022-3816.00033)


## Welfare {.smaller}

Intrepetation:

- The people who give the most inflated, factually wrong answers are often the most confident.

![](images/04_kuklinski_2.png)

:::

## Origins of misinformation? {.smaller}

:::{.nonincremental}

@Jerit2020-px (pp 79-81) review some psychological explanations, emphasizing different cognitive motivations:

- Accuracy motives $\to$ correct decisions
- Directional motives $\to$ consistent decisions

Misinformation is a form of [motivated reasoning]{.blue} reflecting a directional desire to maintain consistency with ones' prior beliefs.

- Directional motives are often the default in politics.

- Identity-linked issues activate them most strongly.


:::

## Correcting Misinformation {.smaller}

- Extensive but theoretically fragmented.

- Depends on the issue, correction, and individuals
  
  - Can you find concrete examples? (p. 83)

- What counts as success?

  - Changed beliefs?
  - Changed attitudes?
  - Both?

- Possibility for corrections to [backfire]{.blue} 

## Backfire Effects {.smaller}

:::{.panel-tabset}

## Overview


- What does it mean for a correction to backfire?

- Why might corrections fail?

  - Correction repeats misinfo, increasing salience
  - Correction threatens worldview/identity triggering directional motives
  
- Compare backfire effects in @Nyhan2015-yi to replication study @Haglin2017-wr

## Nyhan and Reifler (2015)

![](images/04_nyhan_1.png)
![](images/04_nyhan_2.png)

![](images/04_nyhan_3.png)

@Nyhan2015-yi

## Haglin (2017)

![](images/04_haglin_1.png)

![](images/04_haglin_2.png)

![](images/04_haglin_3.png)
![](images/04_haglin_4.png)


@Haglin2017-wr

:::

## Measuring misinformation

- Conceptually, misinformation involves [confidently]{.blue} holding [false]{.blue} beliefs

  - But many studies fail to measure confidence

- Some scholars have proposed that misinformation is a form of [expressive responding]{.blue} or [partisan cheerleading]{.blue} and that partisan gaps disapper when we incentivize correct responses [@Bullock2015-dz]
  
  - If partisan gaps disappear under incentives, are we observing misinformation — or expressive identity signaling?

- What do we make of the directions for further research on p. 88?

## Broader Implications:

Think in terms of larger questions of citizen competence. 

Why does it matter if citizens:

- are confidently wrong?
- are strategically expressive
- update their factual beliefs, but not their subsequent interpretations?
- only correct their beliefs when their identity is not threatened?

# {{<fa lightbulb>}} Next week {.inverse}

## Readings for Political Cognition

By Monday:

- @Zaller1992-mz

By Wednesday:

- @Lodge2013-kq




## References