diff --git a/source/legislation/sec_leg-case-study.ptx b/source/legislation/sec_leg-case-study.ptx index b572a34c..a777886c 100644 --- a/source/legislation/sec_leg-case-study.ptx +++ b/source/legislation/sec_leg-case-study.ptx @@ -1,9 +1,15 @@ -
Case Study: How can we support storytelling using different data sources? +
Now what? Exploring a Case Study + +

+Throughout this chapter, you have explored and asked questions about a data set on anti-trans legislation in the US in recent years. This provides a broad overview of the landscape of anti-trans legislation, where it is happening, and the different aspects of civil rights that have been targeted. As we continue to grapple with what these means for us as global citizens and members of society, it is important to know how to contribute towards the change we want to see in this realm. +

+ + + + How can we support storytelling using different data sources? + -

-Throughout this chapter, you have explored and asked questions about a data set on anti-trans legislation in the US in recent years. This provides a broad overview of the landscape of anti-trans legislation, where it is happening, and the different aspects of civil rights that have been targeted. As we continue to grapple with what these means for us as global citizens and members of society, it’s important to know how to contribute towards the change we want to see in this realm. -

An example of an anti-trans bill that was struck down after legal action and scrutiny is Tennessee’s SB-0001 from 2023. The legislators who supported the bill sought to restrict minors in Tennessee from accessing gender-affirming care. A word-chart of the bill is shown below. Much of the language that lawmakers used surrounded terms like "minors", "performed", and "medical procedure(s)" in reference to gender-affirming care for transgender youth. The term "violation" was used to frame this care in opposition to the laws proposed in the bill. @@ -13,6 +19,9 @@ An example of an anti-trans bill that was struck down after legal action and scr Graph goes here of frequency of words + + +

-->

@@ -23,7 +32,7 @@ Judge Eli Richardson, in his decision on the case, repeatedly argued that "paren the medical care of their children" . This argument highlights the role of parents in seeking basic medical care for transgender youth, and it also uses a commonplace term that was never found in the bill itself: "medical care." In contrast, the downstricken bill referred to medical "procedure(s)," a term chosen to connote experimental, invasive, -and potentially harmful treatments. +and potentially harmful treatments.

@@ -86,8 +95,26 @@ What are other issues you care about that you can learn more about and contribut + + + Resources for learning more, finding community, or taking action +

From Janetta Johnson and Tourmaline recalls how Miss Majors taught folk how to not become overwhelmed by the amount of work left to be done. +

+ +

"“You’d see so many homeless Black trans people sleeping on the street,” Johnson recalls of the Tenderloin in the ’90s. “I was like, ‘How do we deal with this? How do we house people? How do we keep our people safe?’ And [Major’s] like, ‘One person at a time.’” +As a matriarch, she had a way with words — an uncanny ability to flip the perceptions and responsibilities of others with a simple quip or practical observation. Major was skilled, Tourmaline says, at drawing attention away from what could be overwhelming, and demonstrating that even momentary joys could be “a really powerful bridge to what we can build.””

+ — Janetta Johnson and Tourmaline + +
+So let's look towards resources that are building these "momentary joys" and brainstorm how we can support them. +

+ + + + + Legal case details from ACLU. @@ -98,6 +125,11 @@ Court documents and judge’s decision. Narrative sourced from Erin in the Morning. + + + The Family That Miss Major Built + + diff --git a/source/legislation/sec_leg-obtain-clean.ptx b/source/legislation/sec_leg-obtain-clean.ptx index 15ffe941..51fd79f5 100644 --- a/source/legislation/sec_leg-obtain-clean.ptx +++ b/source/legislation/sec_leg-obtain-clean.ptx @@ -1,5 +1,5 @@
- Obtaining and Cleaning the Data + Using Technology to Obtain and Clean the Data: brief Excel and R Studio tutorials

Here, we will find reputable sources for data on anti-trans bills and get the data into a form we can analyze. @@ -17,7 +17,7 @@ - Importing and Merging the Data + Using Excel: Importing and Merging the Data Finding Data Online @@ -125,26 +125,9 @@

Note: The TRIM( ) function will not remove any spaces between two words in a state name (“North Carolina” to “NorthCarolina”), it will just remove excess spaces at the beginning and end of the state name.

- - Exercise -

Instructions: Open the Oklahoma Excel file and click “view raw” to download the file.

-

You are part of a data-cleaning team focused on this data set. Your job is to clean the provided data set as follows: - tracktranslegislation.com -

    -
  1. Delete all conditional formatting (i.e. capitalization)
    2. -
  2. Replace all mentions of “Alabama” with “Oklahoma”
    3. -
  3. Ensure that everything is spelled correctly (use spell check)
    4. -
  4. Remove all duplicate bills
    5. -
  5. Create a new column called “Bill Description 2.” Use the TRIM() function to remove excess spaces in the “Bill Description” column.
    6. -
  6. Your boss wants a new “State2” column where all of the state names are completely capitalized (i.e. “OKLAHOMA”). Create a new “State2” column using the UPPER( ) function.
    7. -
-

-

Note: If you want to know how to clean data using the R language, check out Chapter 5 on Data Structures in RCookbook.

- - - Cleaning for Text Analysis + Cleaning for Text Analysis using R Studio

Text analysis is the process of using computer systems to understand human-written text for analysis purposes including analyzing legislation. Text can be analyzed from unstructured data sources such as emails, surveys, documents, and other online material to extract insights. The main feature of text analysis is training computer software to associate words with meanings, similar to how humans learn a new language through objects, actions, and emotions. Deep learning and natural language processing (NLP) are the principles of text analysis. (Source).

@@ -159,7 +142,8 @@ Note: whenever you download a file to open in R or R Studio, make sure to set your working directory to wherever that file is located. For example, if you have the file just on your desktop (not in any folder), entering “setwd("~/Desktop")” in the console window will set the working directory to your desktop.

- + +

An example of the steps you take to clean text data in R Studio is provided below.

Before we do any text analysis, we have to do some data cleaning. We will use an Arizona bill regarding trans legislation as an example, which can be viewed here .

After downloading the "4-bill-test" excel file, we can enter the following code in R:

@@ -177,7 +161,7 @@

  • -

    Deleting a phrase or line.

    This text tends to have a 1. and a. and (a) and (b). We already eliminated parentheses and periods, so now we are going to eliminate all single letter words and numbers. We are intentionally not removing all numbers. There are several references to K12 or under 18 so we want to maintain these. We also want to make sure that our words will be recognized as the same when we construct relationships, so we have to convert everything to lowercase.

    +

    Deleting a phrase or line.

    This text tends to have a 1. and a. and (a) and (b). We already eliminated parentheses and periods, so now we are going to eliminate all single letter words and numbers. We are intentionally not removing all numbers. There are several references to K12 or under 18 so we want to maintain these. We also want to make sure that our words will be recognized as the same when we construct relationships, so we have to convert everything to lowercase. Don't just copy these commands word for word on your text, think about whether or not you need them. In particular, "str_replace_all("[^[alnum:]\\s]", " ")" will actually delete everything, instead you want to think about what specific words are unnecessary to your data and remove those specific words. For example, maybe "Chapter" appears a lot because your text is broken into "Chapter 1", "Chapter 2", etc. So instead you'd write "str_replace_all("chapter"," ")"

  • @@ -197,7 +181,7 @@

    We can note that the relative frequency (rflel) is the number of times a particular value for a variable has been observed in relation to the total number of values for that variable. We can also see from our top 20 list that "school" and "pronoun" were of the most common words used in the text. This makes sense given the context of this bill.

    - + @@ -245,4 +229,23 @@ + + Exercise +

    Instructions: Open the Oklahoma Excel file and click “view raw” to download the file.

    +

    You are part of a data-cleaning team focused on this data set. Your job is to clean the provided data set as follows: + tracktranslegislation.com +

      +
    1. Delete all conditional formatting (i.e. capitalization)
      2. +
    2. Replace all mentions of “Alabama” with “Oklahoma”
      3. +
    3. Ensure that everything is spelled correctly (use spell check)
      4. +
    4. Remove all duplicate bills
      5. +
    5. Create a new column called “Bill Description 2.” Use the TRIM() function to remove excess spaces in the “Bill Description” column.
      6. +
    6. Your boss wants a new “State2” column where all of the state names are completely capitalized (i.e. “OKLAHOMA”). Create a new “State2” column using the UPPER( ) function.
      7. +
    +

    +

    Note: If you want to know how to clean data using the R language, check out Chapter 5 on Data Structures in RCookbook.

    +     + + +
    • diff --git a/source/legislation/sec_leg-textualize.ptx b/source/legislation/sec_leg-textualize.ptx index a2861be0..656091fa 100644 --- a/source/legislation/sec_leg-textualize.ptx +++ b/source/legislation/sec_leg-textualize.ptx @@ -1,5 +1,5 @@
    - Textualizing the Data + Text as Data Motivating Ideas

    In this section, I will...