---
title: "Slide Deck 3"
author: "Derek Sollberger"
format: revealjs
# format: beamer
editor: visual
---

## Welcome Back!

Are there any lingering questions about the materials so far?

![](question.png)

## Man versus Woman

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::: {.column width="50%"}
![Group A](plot1A.png)
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![Group B](plot1B.png)
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## White vs Hispanic

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::: {.column width="50%"}
![Group A](plot2A.png)
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::: {.column width="50%"}
![Group B](plot2B.png)
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## Masculine vs Feminine

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::: {.column width="50%"}
![Group A](plot3A.png)
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::: {.column width="50%"}
![Group B](plot3B.png)
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## Woman vs Man

(as subjects of the graph)

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::: {.column width="50%"}
![Group A](plot4B_3.png)
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::: {.column width="50%"}
![Group B](plot4A_3.png)
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## Mixed Factors

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![Group A](plot6A.png)
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::: {.column width="50%"}
![Group B](plot6B.png)
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## Small Multiples

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::: {.column width="50%"}
![Group A](Rapp_A.png)
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![Group B](Rapp_B.png)
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::: notes
https://gist.github.com/AlbertRapp/cd81e6237fc737c7360080b31d48f7ea

https://mobile.twitter.com/rappa753/status/1546136701566844928
:::

## Small Multiples

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::: {.column width="50%"}
![Group A](Blakely_B.png)
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::: {.column width="50%"}
![Group B](Blakely_A.png)
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::: notes
https://pietablakely.com/presenting-data-for-a-targeted-universalist-approach/
:::

## Implementation

-   Run this activity (in class?)
-   collect scores and comments between sessions
-   data analysis (i.e. preview semester):
    -   boxplots
    -   significance level

Goal: show audience scores are *not significantly different*

## Inspiration?

![image credit: Axios](refugees_-_Axios.png)

::: notes
:::

## Inspiration!

![image credit: Tanya Shapiro](refugees_-_Tanya_Shapiro.png)

::: notes
https://mobile.twitter.com/tanya_shapiro/status/1499815138521718791
:::

## Story Telling

![image credit: Evelina Judeikyte](refugees_-_Evelina_Judeikyte.png)

::: notes
https://mobile.twitter.com/evelinajud/status/1538911882442166276
:::

## Story Telling

![image credit: Evelina Judeikyte](refugees_-_Evelina_Judeikyte2.png)

::: notes
https://mobile.twitter.com/evelinajud/status/1538911882442166276
:::

## Story Telling

![image credit: Evelina Judeikyte](refugees_-_Evelina_Judeikyte3.png)

::: notes
https://mobile.twitter.com/evelinajud/status/1538911882442166276
:::

## Visualizing Knowledge

![https://www.pbl.nl/en/publications/visualising-knowledge](VisualizingKnowledge_-_1.png)

## Visualizing Knowledge

![https://www.pbl.nl/en/publications/visualising-knowledge](VisualizingKnowledge_-_2.png)

## Visualizing Knowledge

![https://www.pbl.nl/en/publications/visualising-knowledge](VisualizingKnowledge_-_3.png)

## Story Telling

![image credit: Tanya Shapiro](Shapiro_-_abortion_bans.png)

::: notes
https://mobile.twitter.com/tanya_shapiro/status/1546520567980892160
:::

## Further Reading

-   [DataWrapper Blog](https://blog.datawrapper.de/)
-   [One Chart at a Time](https://www.youtube.com/playlist?list=PLfv89tPxlTiVIrwuSBCISiBaGSH1CJR5-) --- YouTube playlist
-   [PBL: Visualizing Knowledge](https://www.pbl.nl/en/publications/visualising-knowledge)

## Thanks to

Consultation with Dr Cat Adams (Unconscious Bias Project)

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![Dr Cat Adams](Cat_Adams.png)
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![Unconscious Bias Project](Unconscious_Bias_Project_logo.png)
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Consultation funded by a UC Merced Anti-Racism grant

## Moving Forward

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![Dr Cat Adams](Cat_Adams.png)
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If implicit bias is detected, let people know that they are not bad people, and we can work on minimizing bias.
:::
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## Moving Forward

![image credit: Danielle Coke](What_history_teaches_us.png)

::: notes
https://mobile.twitter.com/ohhappydani/status/1546969216439435264
:::

## Thank You!