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Firefly reminded me - like the text-reality mix

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reality and its representation in words

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Spider chart, multi parameters over time

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Spider chart, multi parameters over time

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In theory After Effect can handle dataset animation, haven't tried it yet

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A way in to talking about composite text - group speech?

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A way in to talking about composite text - group speech?

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What might a country say if it could speak? (realise we can't do countries...) The improvisational, ethereal and solid entity...
Choral speech, the Chorus

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made of words. This lumbering creature is the sum of our expression

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what is the world (english-speaking) hive-being thinking about today?
- food
- outdoors
- social
- play
Can this character be personified? A giant/cyclops?

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https://towardsdatascience.com/i-tracked-my-happiness-each-day-of-2018-cafe0d048aa2

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just trying the principle... feels too lite

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themed top 50 word lists compared by time

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eg social, plants/outdoors, food

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test: wordart animation, top 50 Hedo happiness words, html mouseover. Unfortunately tooltips not possible. Size of word = Frequency

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two motifs = fish + drop, one as graphic container, one as measurer. We could apply this to pos and neg

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two motifs = fish + drop, one as graphic container, one as measurer. We could apply this to pos and neg

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idea: time series, large/whole dataset, combines area and text

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idea: time series, large/whole dataset, combines area and text

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colours to measure pos and neg word subsets over time 

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colours to measure pos and neg word subsets over time 

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measuring quantities of shared attributes

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measuring quantities of shared attributes

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measuring something over repeated cycles of time

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measuring something over repeated cycles of time

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i like this because it's slightly odd (not sure this is relevant)

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i like this because it's slightly odd (not sure this is relevant)

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could trace a line of affect over a map, with colours fading from most to least

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could trace a line of affect over a map, with colours fading from most to least

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some simple but effective explanations (maybe a summary or leading in to something more complex)

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some simple but effective explanations (maybe a summary or leading in to something more complex)

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traced line/data vis on top of photo hinting at subject

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traced line/data vis on top of photo hinting at subject

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Trace a path of affect_combine photograph

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An examination of translating text to make it as accessible as possible.

Looking at how to make writing easier to read

The following is a toggle that allows you to change whether the text is standard or translated into plain language. Press the p key at any time to switch between standard and plain language versions of the article. If you are using a screen reader, you will need to disable your the quick navigation keyboard commands in order for the P key to function.

Writing text that can be understood by as many people as possible seems like an obvious best practice. But from news media to legal guidance to academic research, the way we write often creates barriers to who can read it. Plain language-a style of writing that uses simplified sentences, everyday vocabulary, and clear structure-aims to remove those barriers.

You can see it in action right here! Click the text next to each paragraph to read it in plain language.

You can see it in action right here! Click the text next to each paragraph to read it in plain language.

You can also use the toggle on the top right to switch everything to plain language. Or use the "p" key on your keyboard.

You can also use the toggle on the top right to switch everything to plain language. Or use the "p" key on your keyboard.

Plain language is useful for everyone, but especially for those who are often denied the opportunity to engage with and comment on public writing. This includes the 20% of the population with learning disabilities, a number of the more than 7 million people in the US with intellectual disabilities (ID), readers for whom English is not a first language and people with limited access to education, among others.

Plain language is helpful for everyone. But it is really good for people who may find other kinds of writing hard to read. That includes:

People with learning disabilities.

People with intellectual disabilities (ID).

People who are learning to speak English.

People who did not go to school or went to school less than they wanted to.

These audiences are routinely excluded from public dialogues, including dialogues about themselves. People with disabilities are also often excluded from writing or sharing their own stories first-hand due to lower vocabulary skills, learning differences, and intellectual disabilities. For example, throughout much of US history, people with ID have had decisions made on their behalf based on the presumption that they do not and cannot understand. This, on top of discriminatory attitudes and stigma, has led to infantilization, institutionalization and eugenic sterilization.

These audiences are routinely excluded from public dialogues, including dialogues about themselves. People with disabilities are also often excluded from writing or sharing their own stories first-hand due to lower vocabulary skills, learning differences, and intellectual disabilities. For example, throughout much of US history, people with ID have had decisions made on their behalf based on the presumption that they do not and cannot understand. This, on top of discriminatory attitudes and stigma, has led to infantilization, institutionalization and eugenic sterilization.

These groups of people get left out of the conversation a lot. Even when the conversation is about them.

For example, many people think people with ID don't understand how to make choices for themselves. Nondisabled people make choices for them. These choices sometimes hurt people with ID. Throughout history, many people with ID have been:

Treated like children.

Forced to live in institutions, group homes, and nursing homes.

Forced to have surgery so they could not have children.

Additionally, there is a tendency to censor content for these audiences rather than explain it, which can contribute to continued disparities, like the higher rate at which people with ID experience sexual violence than nondisabled people.

Additionally, there is a tendency to censor content for these audiences rather than explain it, which can contribute to continued disparities, like the higher rate at which people with ID experience sexual violence than nondisabled people.

Writers will censor writing for these groups. To censor something means to take out information the writer thinks is not appropriate. Taking out information can make some problems worse.

For example, people with ID experience sexual violence more than nondisabled people. But some writers think people with ID should not read about sex or sexual violence. So, readers don't have all the information they need.

The benefits of plain language aren't limited to universally challenging texts like legal documents and tax forms. Even everyday writing, like news articles, can still pose a barrier for some readers.

How does a human assess readability?

Let's walk through how Rebecca, an expert in plain language, translates a text to be more readable. We'll use an excerpt from her translation of a ProPublica article by Amy Silverman in the following example.

Let's walk through how Rebecca, an expert in plain language, translates a text to be more readable. We'll use an excerpt from her translation of a ProPublica article by Amy Silverman in the following example.

Here is an example for how to make writing easier to read. Rebecca wrote this example. She is an expert in plain language. This quote is from a news article. Amy Silverman wrote it for ProPublica. Rebecca wrote it in plain language.

Read the translation below. Click the highlights to see what Rebecca thinks.

Read the translation below. Click the highlights to see what Rebecca thinks.

An example of translating text from standard to plain language where you can select Rebecca's comments to learn more about her translation process.

Plain language translating: side-by-side comparison

PLAIN LANGUAGE

Kyra is autistic and deaf. She was born early. She was very small when she was born. She has trouble seeing, hearing, eating and sleeping. Her hand shakes so she does not do sign language. Her parents think she knows some signs.

Click the highlighted text to see Rebecca's comments.

Click the highlighted text to see Rebecca's comments.

Click the highlighted text to see Rebecca's comments.

More about Rebecca's translation process

When doing a plain language translation, my first step is always to do a close read of the original text. I identify the main points, the order information is presented, and any terms or concepts that I think will need to be defined or replaced. I always think to myself "what does this sentence/idea/concept assume the reader already knows?" There is so much implied in how we write, and plain language should aim to make the implicit more explicit.

When doing a plain language translation, my first step is always to do a close read of the original text. I identify the main points, the order information is presented, and any terms or concepts that I think will need to be defined or replaced. I always think to myself "what does this sentence/idea/concept assume the reader already knows?" There is so much implied in how we write, and plain language should aim to make the implicit more explicit.

My first step is to read the whole paragraph. I look for:

The main points.

The order things are written in.

Any words or ideas that I will need to explain.

A lot of writing assumes the reader already knows something about the topic. Plain language should not assume that. I will explain things fully.

Once I start translating, I typically take a paragraph-by-paragraph approach rather than sentence-by-sentence, because often I will need to re-order information, add definitions or examples, or reintroduce characters and ideas at the top of a new paragraph. Focusing too much on the sentence-level translation can mean losing sight of the bigger picture.

Once I start translating, I typically take a paragraph-by-paragraph approach rather than sentence-by-sentence, because often I will need to re-order information, add definitions or examples, or reintroduce characters and ideas at the top of a new paragraph. Focusing too much on the sentence-level translation can mean losing sight of the bigger picture.

When I write something in plain language, I don't take every sentence from the original writing. I look at the big ideas. I:

Put things in a different order.

Add definitions and examples.

Remind the reader about key characters and ideas.

How do algorithms try to assess readability?

As more people have recognized the practical value of plain language, researchers have sought to quantify the "plainness" of writing through readability formulas-mathematical models that assign numerical scores to text, indicating how understandable they are.

Though most readability formulas were designed to offer rough difficulty estimates for specific groups of readers, their usage varies greatly, with the Agency for Healthcare Research and Quality warning that "these formulas are often interpreted and used in ways that go well beyond what they measure."

Though most readability formulas were designed to offer rough difficulty estimates for specific groups of readers, their usage varies greatly, with the Agency for Healthcare Research and Quality warning that "these formulas are often interpreted and used in ways that go well beyond what they measure."

Readability scores just give us an "estimate" about how hard something is to read. That means the scores are not perfect. But they give us a good idea about how hard it might be for different groups of people to read something.

But the Agency for Healthcare Research and Quality said, "these formulas are often interpreted and used in ways that go well beyond what they measure." This quote means people use these scores in ways they were not made to be used.

Moreover, the simplicity of readability checkers has enabled their widespread adoption. Military engineers use them to help write technical documents. Governments and doctors use them to guide communication for a general audience. Schools and textbook manufacturers use them to tailor reading assignments to particular grade levels and students.

Moreover, the simplicity of readability checkers has enabled their widespread adoption. Military engineers use them to help write technical documents. Governments and doctors use them to guide communication for a general audience. Schools and textbook manufacturers use them to tailor reading assignments to particular grade levels and students.

Readability scores are easy to understand. Many people use these scores to help them write. Some groups that use readability scores are:

To better understand how readability scores work-and how they can fail-let's look at three representative examples.

Algorithm #1: Syllable Count

The Flesch-Kincaid Grade level formula looks, in part, at syllable count, based on the idea that words with fewer syllables are easier to understand.

The Flesch-Kincaid Grade level formula looks, in part, at syllable count, based on the idea that words with fewer syllables are easier to understand.

The Flesch-Kincaid formula measures two things:

How long words are.

How many words are in a sentence.

The formula says the shorter the words and sentence, the easier it is to read.

An interactive showing what the Flesch-Kincaid algorithm considers a easy, medium, and hard sentence. The algorithm deems sentences with lower syllable counts to be easier, so when long multisyllabic words are added (even if they are easy words), the algorithm says it's a hard sentence. If we add short but obscure words, the algorithm thinks it's an easier sentence. We see that the Flesch-Kincaid algorithm isn't able to handle much complexity when it comes to assessing readability.

What happens if we only care about syllables

The below text is at a 2.34 (2nd grade) grade reading level according to Flesch-Kincaid

The two factors considered by Flesch-Kincaid are number of words per sentence and number of syllables per word. This is a short sentence with only two multi-syllable words ( "over" and "lazy"), so the Flesch-Kincaid formula assigns it a low grade level.

More about this algorithm

The author Rudolf Flesch made a career as an early evangelist for plain language in the mid-20th century, promoting his namesake Flesch Reading-Ease Test and its cousin, the Flesch-Kincaid Grade Level Formula, developed in 1975 under contract with the U.S. Navy. It was calibrated on the reading comprehension scores of 531 enlisted Navy personnel, in order to measure readability in the specific context of technical communication. Today, perhaps thanks to its misleading name, the F-K Grade Level Formula is used in schools to estimate reading difficulty for children, overlooking the obvious fact that elementary school students and naval cadets may not agree on whether the same text is easy or difficult to understand.

The author Rudolf Flesch made a career as an early evangelist for plain language in the mid-20th century, promoting his namesake Flesch Reading-Ease Test and its cousin, the Flesch-Kincaid Grade Level Formula, developed in 1975 under contract with the U.S. Navy. It was calibrated on the reading comprehension scores of 531 enlisted Navy personnel, in order to measure readability in the specific context of technical communication. Today, perhaps thanks to its misleading name, the F-K Grade Level Formula is used in schools to estimate reading difficulty for children, overlooking the obvious fact that elementary school students and naval cadets may not agree on whether the same text is easy or difficult to understand.

Rudolf Flesch was an author. He lived in the 1900s. He thought plain language was very important. He made two scores to measure how easy something was to read:

The Flesch Reading-Ease Test

The Flesch-Kincaid Grade Level Formula

Rudolf Flesch created the Flesch-Kincaid Grade Level Formula when he was working for the Navy. He measured how well 531 people in the Navy could read technical communications. Technical communications have special information that people in the Navy need to do their jobs.

Now we use the Flesch-Kincaid Grade Level Formula to measure readings for children. But the score has never been tested with children.

Algorithm #2: Difficult words

The Dale-Chall Readability Formula considers the proportion of difficult words, where it deems a word "difficult" if it is not on a list of 3,000 words familiar to fourth-grade students.

The Dale-Chall Readability Formula considers the proportion of difficult words, where it deems a word "difficult" if it is not on a list of 3,000 words familiar to fourth-grade students.

Dale-Chall is another readability score. It measures two things:

How long each sentence is.

The number of easy or hard words.

Dale-Chall uses a list of 3,000 easy words. Dale-Chall says these are words most 4th graders know. Any other word is a hard word.

One risk in the use of vocabulary lists is that the vocabulary of the readers surveyed to create them may not match the vocabulary of the intended audience. The original Dale-Chall list of "familiar words" was compiled in 1948 through a survey of U.S. fourth-graders, and even the most recent update to the list in 1995 retains obsolete words like "Negro" and "homely" while omitting "computer."

One risk in the use of vocabulary lists is that the vocabulary of the readers surveyed to create them may not match the vocabulary of the intended audience. The original Dale-Chall list of "familiar words" was compiled in 1948 through a survey of U.S. fourth-graders, and even the most recent update to the list in 1995 retains obsolete words like "Negro" and "homely" while omitting "computer."

But not everyone knows the same words. Dale-Chall made the first easy word list in 1948. They updated the list in 1995.

We don't use the same words now that we did back then. The list has words most people don't use now, like "Negro" and "homely." And it doesn't have words a lot of people use now, like "computer."

An interactive showing what the Dale-Chall algorithm considers a easy, medium, and hard sentence. Dale-Chall considers average sentence length along with how many difficult words are used, where "difficult" words are words that don't appear on the Dale-Chall list. If we add words that are unfamiliar (like dinosaur or dude) the algorithm says it's a difficult sentence. If we simply add a sentence that just contains the exclamation "Yes!", that lowers the average sentence length and makes the algorithm say it's an easier sentence. We see that the Dale-Chall algorithm isn't able to handle much complexity when it comes to assessing readability.

What happens if we only care about "difficult" words

The below text is at a 0.45 (4th grade or below) grade reading level according to Dale-Chall

Dale-Chall considers average sentence length along with percentage of difficult words (PDW), where "difficult" words are words that don't appear on the Dale-Chall list. This is a short sentence made entirely of words on the Dale-Chall list, so its ASL score is low and its PDW score is zero, yielding a score of 0.45. Since the formula was calibrated on a group of 4th grade students, all scores below 5.0 are collapsed into a single readability category representing "4th grade and below."

Algorithm #3: Algorithmic black boxes

More recently, US schools and textbook manufacturers have standardized their curricula on the Lexile Framework for Reading, a set of readability algorithms and vocabulary lists designed to automatically match students with appropriately difficult books. Publishers apply Lexile to their texts to rate their difficulty. A Lexile score of 210 indicates an easy-to-read book, while a score of 1730 indicates a very challenging one. A reading comprehension test assigns a corresponding reading score to each student, after which teachers pair students with texts that have comparable Lexile scores.

More recently, US schools and textbook manufacturers have standardized their curricula on the Lexile Framework for Reading, a set of readability algorithms and vocabulary lists designed to automatically match students with appropriately difficult books. Publishers apply Lexile to their texts to rate their difficulty. A Lexile score of 210 indicates an easy-to-read book, while a score of 1730 indicates a very challenging one. A reading comprehension test assigns a corresponding reading score to each student, after which teachers pair students with texts that have comparable Lexile scores.

Lexile Framework for Reading is another formula to measure how easy something is to read. Schools and textbook writers use Lexile. Lexile helps match students with books they are able to read.

Here is how it works:

Lexile scores books. It rates them as easy or hard to read.

Students take a reading test. It tells them how well they read.

Teachers give students books that won't be too easy or too hard for them to read.

The approach sounds simple enough, but critics have pointed out absurdities in Lexile's results. According to Lexile, The Grapes of Wrath (Lexile score: 680) is easier to understand than the Nancy Drew mystery Nancy's Mysterious Letter (score: 720), but neither of these is as challenging as The Library Mouse (score: 860), a 32-page illustrated children's book.

The approach sounds simple enough, but critics have pointed out absurdities in Lexile's results. According to Lexile, The Grapes of Wrath (Lexile score: 680) is easier to understand than the Nancy Drew mystery Nancy's Mysterious Letter (score: 720), but neither of these is as challenging as The Library Mouse (score: 860), a 32-page illustrated children's book.

But the scores Lexile gives books don't always make sense. Here's an example. Lexile says:

Lexile says that The Grapes of Wrath is a pretty easy book to read. The Grapes of Wrath is a book for adults. It is over 400 pages long and has many complex themes and ideas.

Lexile says the Nancy Drew book Nancy's Mysterious Letter is harder to read than The Grapes of Wrath. Nancy Drew books are made for preteens. Most people would consider it much easier to read than The Grapes of Wrath.

Lexile says The Library Mouse is harder to read than the Nancy Drew book or The Grapes of Wrath. The Library Mouse is a picture book for children.

It does not make sense that a children's picture book would be harder to read than a book for adults.

Images of three book covers arranged by how difficult the Lexile algorithm thinks they are. It says Grapes of Wrath is the easiest, followed by Nancy's Mysterious Letter, and the hardest is The Library Mouse, a 32-page illustrated children's book. This doesn't make much sense, most people would say that Grapes of Wrath is much more difficult than a simple children's book.

How Lexile scores popular books for children

How exactly are Lexile scores calculated? Unfortunately for us, the Lexile Framework is the intellectual property of MetaMetrics, the private company that created it, so we can only guess at the secret recipe, but it's a pretty good bet that Lexile scores depend on a mixture of the same factors used in Flesch-Kincaid and other open-source readability measures.

How exactly are Lexile scores calculated? Unfortunately for us, the Lexile Framework is the intellectual property of MetaMetrics, the private company that created it, so we can only guess at the secret recipe, but it's a pretty good bet that Lexile scores depend on a mixture of the same factors used in Flesch-Kincaid and other open-source readability measures.

We don't know how Lexile decides how hard each book is. MetaMetrics is the company that made Lexile. They do not share what math they do to get their readability scores. But it is probably close to the other formulas we looked at. We know Lexile looks at:

How long words are.

How long each sentence is.

How familiar the words are.

If things are repeated.

Formulas based on surface level text properties and word frequency have clear limitations. None of them consider how well organized the information is, or whether the sentences and paragraphs are coherent. None consider the role of grammatical tense. None account for the explanation of acronyms and jargon. None would balk at Jack Torrance's rambling and meaningless draft in The Shining, endlessly repeating "All work and no play makes Jack a dull boy."

Formulas based on surface level text properties and word frequency have clear limitations. None of them consider how well organized the information is, or whether the sentences and paragraphs are coherent. None consider the role of grammatical tense. None account for the explanation of acronyms and jargon. None would balk at Jack Torrance's rambling and meaningless draft in The Shining, endlessly repeating "All work and no play makes Jack a dull boy."

None of these formulas are perfect. They don't measure:

The order of the sentences.

If the writing makes sense.

Grammar.

If the writer explains hard words and phrases.

These formulas would have no problem with the scene in The Shining where Jack Torrance writes "All work and no play makes Jack a dull boy," over and over again. They do not check if the writing means anything.

But proprietary tech like Lexile has some of the most disconcerting aspects of both worlds. As flawed as Flesch-Kincaid or Dale-Chall, but opaque and unexplainable. The main benefit of the F-K and D-C formulas (and other simple algorithms like Gunning-Fog and SMOG) is their transparency. A broken system locked in a black box can't even offer this.

Where Do We Go From Here?

In recent decades, as disability activists have won more civil rights, both in the US and internationally, accessible writing has gained greater attention. And with this attention comes the very real possibility that plain language will be outsourced to blackbox technologies that are grounded in antiquated data.

In recent decades, as disability activists have won more civil rights, both in the US and internationally, accessible writing has gained greater attention. And with this attention comes the very real possibility that plain language will be outsourced to blackbox technologies that are grounded in antiquated data.

People with disabilities have more rights than ever before. More people are thinking about how to write for all kinds of readers. And so more people may try to use formulas like Flesch-Kincaid or Dale-Chall. But these formulas have problems.

Technology alone isn't the answer. Even the most thoughtful algorithms and robust data sets lack context. Ultimately, the effectiveness of plain language translations comes down to engagement with your audience. Engagement that doesn't make assumptions about what the audience understands, but will instead ask them to find out. Engagement that's willing to work directly with people with disabilities or limited access to education, and not through intermediaries. As disabled advocates and organizations led by disabled people have been saying all along: "Nothing about us without us."

Technology alone isn't the answer. Even the most thoughtful algorithms and robust data sets lack context. Ultimately, the effectiveness of plain language translations comes down to engagement with your audience. Engagement that doesn't make assumptions about what the audience understands, but will instead ask them to find out. Engagement that's willing to work directly with people with disabilities or limited access to education, and not through intermediaries. As disabled advocates and organizations led by disabled people have been saying all along: "Nothing about us without us."

We can't just use technology to make writing easier to read. We have to think about who we are writing for. We have to include people with disabilities when writing in plain language. It is like disabled people say: "Nothing about us without us."

Authors' note

A lot of people helped us write this article.

Thank you to Zoe Gross and Leah Mapstead for being our expert readers. Zoe and Leah told us how to make the article better. Zoe is Director of Advocacy at the Autistic Self-Advocacy Network. Leah is a disability advocate and actor.

Thank you to:

Rob Smith

Michelle McGhee

Matt Daniels

Russell Samora

The rest of the team at The Pudding

They made our article interactive. They helped brainstorm ideas. They told us how to make our writing better.

Thank you to Matt Hackert for making sure the article works on a screen reader. Matt leads the Center of Excellence in Nonvisual Accessibility at the National Federation of the Blind.

Thank you to Amy Silverman for helping come up with the idea for this article.

You can learn more about Kyra here.

You can learn more about how to write plain language and Easy Read here, here, and here.

The Pudding is a digital publication that explains ideas debated in culture with visual essays.

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Looking at how to make writing easier to read