Gender on Visualizing.JP

Couldn't Count It, So Drew the Pathways: Beyond XX and XY

Sat, 07 Nov 2026 00:00:00 +0900

Beyond XX and XY is a graphic that ran as a double-page spread in the September 2017 issue of Scientific American. It draws sex not as a point on a single line but as a pathway of development, from conception through puberty and beyond. Its author, Amanda Montañez, first planned a graphic that would show frequencies with data. Because reliable frequency data could not be found, she changed course, from a graphic of numbers to one that shows the complexity itself.

This article follows the printed graphic (pp.50–51) and Montañez’s making-of post, “Visualizing Sex as a Spectrum” (August 29, 2017). It also draws on the project page of Pitch Interactive, which handled the design, and on the letters column of the January 2018 issue. Because this graphic touches on medicine and the diversity of sex, I keep the graphic’s own text, the author’s words, and the criticism separate, so that it is clear whose words each one is.

2017: A graphic that was meant to show numbers

The September 2017 issue was a special issue on sex and gender. The graphic is a spread beginning on page 50. Montañez is a graphics editor at the magazine and has a background as a medical illustrator. According to her making-of post, the original plan was this.

“The project was originally conceived as a data-driven graphic exploring the spectra of sex and gender.”

In other words, the plan was a graphic that would explore the range of sex and gender through data. She considered whether it could show how frequent transgender and nonbinary people are, what share of people are intersex, and the breakdown by type of DSD (disorders of sex development).

The research was done by researcher Amanda Hobbs. What came back was less an answer than “a series of new questions.” Montañez gives two reasons. Surveys tend to lump transgender people together with gay, lesbian, and bisexual people. And DSD is defined differently by different institutions, and a condition may go unfound or be discovered only later.

“The project abruptly transformed into an exercise in visualizing complexity.”

The plan changed at once into an exercise in showing complexity, in this sentence’s terms. Montañez goes on to write that sex, gender, and sexuality are each a separate spectrum, and that sex cannot be drawn on a one-dimensional scale; she shifted the graphic’s main focus to intersex.

That the numbers are not settled also shows in how the figures differ between sources.

Source	Share of intersex people	Premise / basis
Montañez’s making-of post (2017)	about 1% (about one percent)	When DSD is defined broadly
Data Feminism (2020)	an estimated 1.7%	Note 55 cites Fausto-Sterling as the source
Jerry Coyne’s blog (2022)	about 0.018% (1 in 5,600)	Based on Sax (2002)

The text of the finished graphic contains no figure for the share.

From a hand-drawn outline to the spread

Montañez first drew the content on paper. Laying out every known DSD proved too “unwieldy” for one spread, so she picked several conditions and made a “visual outline” tracing the paths of development. It was not pretty, she writes, but it captured the tangled feel that the subject calls for.

On a white background, a horizontal band runs from “Typical biological female” at the left end to “Typical biological male” at the right end, with explanatory text arranged in rows for conception, birth, puberty, and adulthood, joined by curved lines

Source: Amanda Montañez / Scientific American, “Visualizing Sex as a Spectrum” (© Scientific American)

The accuracy of this outline was checked by Amy Wisniewski, a DSD expert at the University of Oklahoma. I follow the spelling in the graphic’s credit; in the body of the making-of post it is “Winsiewski.” The design was handled by Pitch Interactive, a data visualization studio in Oakland.

On a black background, a band runs from “Typical biological female” at the left end to “Typical biological male” at the right end, with boxes of explanation arranged in rows labeled Conception, Birth, and Puberty, joined by thick arrows

Source: Pitch Interactive / Scientific American, “Visualizing Sex as a Spectrum” (© Scientific American)

Even after the look was settled, she says, colleagues kept pointing out confusing spots, and the text and design kept being revised. Comparing the three images in the making-of post shows these differences.

Item	Outline	Pitch’s sketch	Finished printed page
Vertical rows	Conception, birth, puberty, adulthood	Conception, birth, puberty	Conception, birth, puberty
Heading of the central band	DSD examples	DSD examples	Intersex conditions
Factors that determine sex	In the opening sentence	In the opening sentence	Set up as a column at the left edge
5-alpha reductase deficiency	Treated like the others	Treated like the others	Highlighted with a dotted frame and a circle of explanation
Definition of gender	Below the graphic	Below the graphic	Above the graphic

The heading changed from “DSD” to “intersex.” The making-of post does not say why it was changed.

What the finished graphic puts where

The finished graphic is not a picture of quantitative data. It is a conceptual diagram, a flowchart, that illustrates the factors involved in sex differentiation and the pathways followed by several DSDs.

A double-page spread on a black background. A title and body text sit at the top, and a column of five factors that determine sex runs down the left edge. Above and below a band running horizontally through the center, boxes of explanation are arranged in rows for conception, birth, and puberty, joined by many arrows

Element of the graphic	What it represents	Basis
Vertical direction	Developmental time. Three rows: CONCEPTION, BIRTH, PUBERTY	Printed page; Pitch (three pivotal milestones)
Left-hand column	Factors that determine sex: chromosomes, genes, hormones, internal and external genitalia, secondary sex characteristics	Printed page
Horizontal band	The two ends are Typical biological female and male. The names of conditions lie in between. There is no scale	Printed page; Pitch
Change of color along the band	The spectrum of sex	Data Feminism
Boxes of explanation	The condition at each row. The line at the left edge of a box can be read as corresponding to a factor in the left-hand column	Printed page
Branching arrows	The pathway each condition follows	Printed page; Data Feminism
Boxes for medical intervention	Surgery and hormone therapy. Example: “Genitals can be modified to look more typically female”	Printed page
Dotted frame and the circle at upper right	Follows 5-alpha reductase deficiency through the graphic as a single example	Printed page

According to the circle at upper right, a person with 5-alpha reductase deficiency is 46XY, the appearance at birth varies from person to person, and the person can be assigned to either sex. At puberty testosterone rises and male characteristics appear, and the circle says that people raised as girls often come to identify as male. By having the reader follow one condition row by row, the graphic can be read as showing a change that cannot be drawn with a single point.

Words in the graphic, words of the author, words of the studio

Three sources of words are at work in this graphic. Statements of different strength sit side by side, so I set them out separately.

Whose words	Where	What it says
Text of the graphic (signed by Montañez)	Printed page	What it shows is “A small cross section.” Sex and gender “appear to exist on a spectrum”
Pitch Interactive	Project page	“sex assignment and gender identity lies on a spectrum”
Montañez	Making-of post	DSD is an “evidence-based argument” against fixed assignment of sex and gender. If understanding spreads, policy could also follow “social justice”
Montañez	BBMRI-ERIC webinar announcement (2022)	A subject for which she had never seen a visualized example, and which was more complex than she had imagined

The graphic’s text is restrained, saying “appear to,” and states that it shows only a cross section. Pitch asserts “lies on.” The claims about society and policy are on the making-of post’s side, and they carry a note that these are the author’s personal views. The criticism discussed below can be read as aimed at different ones of these three.

How third parties saw the graphic

The evaluations each point at a different aspect. Except for the book, all of them are letters, blog posts, or social media posts, not peer-reviewed papers.

The first is chapter 4 of Data Feminism (Catherine D’Ignazio and Lauren F. Klein, 2020). Just before this graphic, the book gives two examples of rearranging binary data from the inside: The Telegraph’s “Born Equal. Treated Unequally.” and a Guardian piece. It then takes up this graphic as a question of how to draw gender itself.

“Visualization is often thought of as a way to reduce complexity, but here it operates in the reverse” (p.115)

The assessment is that visualization is usually thought of as a way to reduce complexity, but this graphic works the other way around. The book also writes that it made visible to a wide audience what scientists already knew.

The second is a letter from Kimberly Zieselman, executive director of the advocacy group interACT (January 2018 issue). She acknowledged that the graphic helps the visibility of intersex people, and then wrote this.

“There is just one thing we wish it had shown intersex is not: in need of ‘fixing.’”

Her point is that she wished it had shown that intersex is not something to be “fixed.” The graphic indicates where surgery can alter the genitals and gonads can be removed, but it does not mention consequences such as scarring, chronic pain, and infertility. She also writes that surgery is not needed to raise a child as a boy or a girl.

In the same column, Montañez answered that the scope of the graphic was the science of the spectrum and that it could not cover health effects. She said she included surgery because many people actually undergo it. In the making-of post, moreover, Montañez cites calls to stop medically unnecessary surgery on infants as one example of a change she would welcome.

The third is the 2022 criticism from the “sex is binary” position. It began on November 14, when the physicist Sean Carroll replied to a post by Colin Wright, “Biological sex is real, immutable, and binary.”, with this graphic attached and the words “Actual science would like a word.”

Jerry Coyne wrote that what the graphic shows is the complexity of the pathways of sex-related traits, and that gametes are not drawn. Emma Hilton asked why a clearly male condition is placed on the “more female” side of a female condition. Earlier, in August, Zach Elliott had written this on social media.

“There are no units on this spectrum to quantify it.”

The point is that the spectrum has no units. Elliott also raised the lack of an objective way to decide where each condition is placed, and the conflation of the mechanisms of sex determination with sex itself.

Who criticized	Aimed at	Point	The author’s reply
interACT (2018)	The medical-intervention boxes	Does not show the outcomes of surgery. Surgery is not needed for raising a child	It was outside the scope. Included because many people undergo it
The “sex is binary” position (2022)	The horizontal band and the surrounding text	Elliott: no units and no objective basis for placement; mechanisms are conflated with sex. Hilton: the left-right placement of conditions. Coyne: gametes are not drawn; the direction of policy and science is reversed	Not found in the sources for this article

The former questions how the graphic handles the interventions it drew. Of the latter, Elliott and Hilton question what the horizontal band, as an axis, means. Coyne questions what the graphic does not draw, and the claim in the making-of post.

Takeaways

There are four judgments that can be carried back into visualization work.

The first is to treat the unavailability of data as grounds for changing the graphic’s approach. Montañez gave up the frequency graphic and switched to a pathway graphic, and stated the limits of its scope in the text with “A small cross section.” When numbers cannot be shown, it can be read that the graphic needs to say what it shows instead.

The second is how to handle an axis that looks like a quantity when it is used in a conceptual diagram. The horizontal band has no scale, but “Typical” is written at both ends and the conditions line up left to right. Elliott’s point about units and Hilton’s question about placement can be read as aimed at the lack of any stated basis for an axis that looks quantitative. If an ordered axis is used, it can be read that one needs to write down even what the positions do not mean.

The third is that the elements one draws raise questions about what one did not draw. Placing the medical-intervention boxes led to the point that their outcomes are not drawn. Whether to write the line drawn around the scope close to the graphic can be read as part of the design.

The fourth is that the choice of words is also part of the graphic. The band’s heading changed from “DSD examples” to “Intersex conditions.” The educators’ site Gender-Inclusive Biology (2021) recommends “intersex traits” instead. It can be read that a single word of a label may change how the graphic is read.

In its December 2024 issue the same magazine also ran a visualization of progress toward gender equality, which turns index values into a graphic. The two can be read side by side, one shown with numbers and the other where numbers could not be shown.

References

Primary sources

Amanda Montañez, “Beyond XX and XY: The Extraordinary Complexity of Sex Determination,” Scientific American 317(3), September 2017, p.50. doi:10.1038/scientificamerican0917-50. Article
Amanda Montañez, “Visualizing Sex as a Spectrum,” SA Visual (Scientific American Blog Network), 2017-08-29. Blog
Table of contents of the September 2017 issue of Scientific American. Table of contents
Pitch Interactive, “Beyond XX and XY.” Project page
“Readers Respond to the September 2017 Issue” (Kimberly Zieselman’s letter and Montañez’s reply), Scientific American 318(1), January 2018, p.5. Article
BBMRI-ERIC, “Visualizing the Complexity of Sex Determination” (webinar announcement, 2022-11-16). iCal / Date page
An episode of the BBMRI-ERIC Podcast (recording of the webinar, audio). Episode

Secondary sources

Catherine D’Ignazio and Lauren F. Klein, Data Feminism, MIT Press, 2020, ch.4 “What Gets Counted Counts,” pp.113–117, notes 52–55 (CC BY-NC-ND 4.0). Internet Archive / OCR text
Letter by Kimberly Zieselman (interACT: Advocates for Intersex Youth), in the Letters above. Article
Sam Long, “Scientific American: “Beyond XX and XY”,” Gender-Inclusive Biology, 2021-02-25. Article
Jerry Coyne, “Stephen Knight on Sean Carroll, Colin Wright, and the binary of sex,” Why Evolution Is True, 2022-11-16 (quotes posts by Sean Carroll and Emma Hilton). Article
Jerry Coyne, “Once again, the binary nature of biological sex: Colin Wright and Sean Carroll,” Why Evolution Is True, 2022-12-06. Article
Thread by Zach Elliott (@zaelefty), August 2022. Thread Reader
Post by Sean Carroll (2022-11-14). X / Post by Emma Hilton (2022-11-14). X

Dropping Pink and Blue, Giving Women the Purple That Stands Out on White: The Telegraph's "Born Equal. Treated Unequally."

Sat, 07 Nov 2026 00:00:00 +0900

For its charts on gender inequality, The Telegraph dropped pink and blue and used the purple and green of the suffrage movement. Which color went to which sex was decided by how they look against a white background: purple, which stands out more than green on white, went to women. The interactive piece with this palette, “Born Equal. Treated Unequally.,” was published on 8 March 2018, International Women’s Day.

The Telegraph piece itself is for subscribers only, and I have not been able to read it. What follows about the charts comes through two sources: Chapter 4 of Data Feminism (2020) by Catherine D’Ignazio and Lauren F. Klein, which shows the work as Figure 4.6, and a Datawrapper blog post (10 July 2018) in which Lisa Charlotte Muth put questions to Fraser Lyness, who chose the colors.

8 March 2018: A Piece for International Women’s Day

According to Data Feminism, in March 2018 reporters on The Telegraph’s Lifestyle Desk were thinking about how to mark International Women’s Day. What caught their eye was the gender gap in the UK across education, politics, business, and culture.

Item	Detail	Source
Title	“Born Equal. Treated Unequally.” (Datawrapper writes “Born equal. Treated unequally”)	Data Feminism note 48, Datawrapper
Publication date	8 March 2018	Data Feminism note 48
Text authors	Claire Cohen, Patrick Scott, Ellie Kempster	Same
Production credits	The three above, plus Richard Moynihan, Oliver Edgington, Dario Verrengia, Fraser Lyness, George Ioakeimidis, Jamie Johnson	Data Feminism Fig. 4.6
Who chose the colors	Fraser Lyness (Director of Graphic Journalism) and his team	Datawrapper

The article’s URL contains “women-mean-business-interactive.” In an October 2018 article, The Telegraph’s Jamie Johnson writes that on International Women’s Day the paper launched “Women Mean Business,” a campaign to support women entrepreneurs. The same article also describes 2018 as the year of celebrating 100 years of women’s suffrage. I have found no source, however, saying that this piece was timed to that centenary.

According to Data Feminism, the data from other institutions that the reporters used all came in a male/female binary. The categories could not be changed. What could be changed was the color.

Borrowing the Suffrage Movement’s Purple and Green

The quickest colors for separating men from women are pink and blue. In the Datawrapper post, Lisa Charlotte Muth cites a “Chart Doctor” column by Alan Smith of the FT (I have not read the original column; I checked it against the passage the Datawrapper post quotes). Smith wrote that he used pink and blue because the link between color and sex is so strong that no legend is needed, which keeps the chart clean. Muth writes that pink and blue reinforce gender stereotypes and are especially counterproductive in charts about inequality.

Data Feminism makes the same point.

“Pink and blue is, after all, another hierarchy, and the goal of the Telegraph team members was to mitigate inequality, not reinforce it.”

In short, pink and blue is itself a hierarchy, and the team’s aim was to ease inequality, not to reinforce it.

The team chose purple and green. In Lyness’s account, these were the colors of the “Votes for Women” campaign in early twentieth-century Britain.

“At the heart of this movement were “Sylvia Pankhurst and Emmeline Pethick-Lawrence and essentially the colors were their choice (apparently symbolizing; purple for freedom and dignity, white for purity, green for hope).””

The colors were chosen by Sylvia Pankhurst and Emmeline Pethick-Lawrence, who stood at the center of the movement, and are said to stand for freedom and dignity (purple), purity (white), and hope (green). Of these, the team used purple and green for the sexes.

Posters, a sash, badges, a ticket, and a book cover printed in the three colors of purple, white, and green for “Votes for Women”

Source: Lisa Charlotte Muth, Datawrapper Blog

Lyness says the reference to the suffrage movement “sealed the deal.”

“Having a reference to the suffrage movement helped seal the deal”

Nobody objected to the fact that, without pink and blue, the sex of a group is not obvious at a glance, he says. Rather, Lyness answers, everyone felt that pink and blue would undermine the message they wanted to convey.

Purple, Which Stands Out on White, for the Smaller Group

The purple-and-green pair was chosen for what it means. Then which sex gets the purple? Lyness answers:

“Against white, purple registers with far greater contrast and so should attract more attention when putting alongside the green, not by much but just enough to tip the scales. In a lot of the visualisations men largely outnumber women, so it was a fairly simple method of bringing them back into focus.”

On a white background, purple has much stronger contrast and draws attention when placed next to green. The difference is not large, but it is enough to tip the scales. Because men far outnumber women in many of the charts, it was a simple way to bring women back into focus. Before this, Lyness also says, they had wanted to give priority to women.

The contrast is “far greater,” but the pull on attention is “not by much.” This can be read as the idea that the effect of making something stand out can be modest; it is enough for the scales to tip a little.

Datawrapper shows the following image as a chart where this effect is easy to see.

On a white background, a large green circle for 757 plaques (86%) for men and a small purple circle for 127 plaques (14%) for women sit on either side of a vertical line

Source: The Telegraph / Datawrapper Blog

The header reads “Women Mean Business” and “ENGLISH HERITAGE BLUE PLAQUES.” Of 884 blue plaques, 757 are for men and 127 for women. Measuring the image, the ratio of the circles’ areas can be read as close to the ratio of the counts (about 6 to 1). Datawrapper does not say which part of the piece this image comes from.

Figure 4.6 of Data Feminism lines up FTSE 100 CEOs as human-shaped icons filled in purple and green. The accompanying text reads “Just six of FTSE 100 CEOs are women.”

There is one more purple-and-green chart, for which the source article is not named.

A bar chart showing the distribution of companies’ gender pay gaps. Bars where women fall below are green, bars where they are above are purple, and bars near zero are gray; green bars make up most of the chart

Source: The Telegraph / Datawrapper Blog

It shows the gap between men’s and women’s median hourly pay across all companies, as a distribution of the number of companies. In the legend, “Women trail men” is green and “Women lead men” is purple. Here the colors stand for the direction of the gap, not for sex, but the idea of making the smaller side purple can be read as the same. Datawrapper writes that the same palette was also used in a pay-gap article (paywalled) of 22 March 2018.

Data	Visual channel	Specifics	Source
Sex	Hue	Women = purple, men = green	Datawrapper, Data Feminism
Male/female CEOs	Human-shaped icons	Purple and green, legend “Men / Women”	Data Feminism Fig. 4.6
Number of plaques	Circle size	Men 757 (green), women 127 (purple)	Datawrapper image
Direction of pay gap	Bar color	Below = green, above = purple	Datawrapper image
Color values	n/a	Not confirmed	n/a

What Colors Did Other Newsrooms Use?

The Datawrapper post says that Muth’s piece grew out of a tweet by the FT’s John Burn-Murdoch (I have not read the tweet). The tweet lined up the colors that outlets used in reporting UK pay-gap data.

Category	Examples (according to the Datawrapper post)
Pink and blue (red and blue)	NYT (2015, 2017), Bloomberg (2016), WSJ (2016), Daily Mail (2018), ZEIT (2016, 2018), two of The Pudding’s film pieces (2017)
Other colors	Economist, Guardian, FT, Telegraph, Bloomberg (all 2018), Morgenpost (2017), Washington Post (2017), BBC (2017, 2018), and others
Inconsistent within one outlet	Bloomberg’s four pieces in 2018 each use different colors. Quartz swaps the pink-and-blue assignment from article to article

For another work by The Pudding, see the Women’s Pockets article in the same series.

Part of the charts from outlets that did not use pink and blue. The sexes are colored in various pairs, such as teal and vermilion, purple and brown, orange and navy

Source: charts by each outlet / Lisa Charlotte Muth (Datawrapper Blog)

Muth writes that the Economist, Guardian, Telegraph, Washington Post, and others gave women a cooler color than men, and remarks, “You can’t go further away from the norm.” On the other hand, she calls swapping the assignment, as Quartz does, risky: readers who see pink and blue do not check the legend, so women may be read as the better-off group.

How Third Parties Saw This Palette

The first, Muth, praises the fact that the reasoning could be put into words. Lyness did not simply pick two pretty colors; he could explain why, she writes, and she titled the section “How to choose colors: The Telegraph case.”

The second, Data Feminism, cites the piece as an example of a situation where the makers cannot choose how the data is collected and can only “hack” binary data from the inside.

“Here, one hierarchy—the hierarchy in which colors are perceived by the eye—was employed to challenge another one: the hierarchy of gender.”

The assessment is that a hierarchy of visible color was used to challenge a hierarchy of gender. The book adds, however, that the team could have gone one step further.

“They had an opportunity to communicate to the public that gender is not a binary by spelling that out—in the text of the story or in a caption under the graphics or by showing visually that there was no data for nonbinary people.”

The criticism is that they could have told the public that gender is not a binary, in the text, in a caption, or by showing in a graphic that there was no data on nonbinary people.

The contrast the book draws is The Guardian’s “Does the New Congress Reflect You?” (2018). It has three categories: cis male, cis female, and trans + nonbinary. Choosing trans + nonbinary turns every district gray and displays “0 people in Congress are like you.” The book writes that the absence of data becomes an important conclusion in itself. The same section then moves on to how to depict gender itself, taking up a figure by Amanda Montañez of Scientific American (the Beyond XX and XY article).

The third is the research side. In a 2024 paper analyzing gender visualizations from 2012 to 2022, Cabric et al. wrote that alternative palettes such as purple and green “remain uncommon.” They do not name The Telegraph; the mention comes by way of Muth’s post. They also list the option of dropping color and distinguishing by label or position.

Takeaways

There are four judgments to take away.

The first is to separate the reason for choosing the colors from the reason for assigning them. The pair was chosen for its meaning and the assignment for how it looks. Because there were two reasons, it can be read as the team being able to push through, internally, the decision to give up pink and blue, which need no legend.

The second is to give the more eye-catching color to the smaller group. The luminance difference from the background can be compared with measures such as the WCAG ratio or APCA, covered in the color contrast article. These, however, are measures of text legibility, not of how easily something attracts attention. That the effect need only be “not by much” is Lyness’s own phrasing.

The third is to keep the assignment fixed within a publication. If you swap it, as Quartz does, it will be read in the opposite sense.

The fourth is to remember that changing colors does not change the categories. If you have only binary data, you can state that limit in the body text or in a caption. There is also the route, as in The Guardian, of showing the absence of data in the graphic itself.

References

Primary sources

Claire Cohen, Patrick Scott, Ellie Kempster et al., “Born Equal. Treated Unequally.,” The Telegraph, 2018-03-08 (subscribers only; not read by the author). Article
Fraser Lyness’s remarks (in Lisa Charlotte Muth’s Datawrapper post). Article
Jamie Johnson, “Women Mean Business: How the Telegraph’s campaign unfolded,” The Telegraph, 2018-10-31 (Yahoo reprint). Article
The Telegraph’s pay-gap article using the same palette, 2018-03-22 (subscribers only; not read by the author). Article

Secondary sources

Lisa Charlotte Muth (then Rost), “An alternative to pink & blue: Colors for gender data,” Datawrapper Blog, 2018-07-10. Article (old URL: https://blog.datawrapper.de/gendercolor/)
Alan Smith, FT “Chart Doctor” column (on the pink-and-blue baby names chart). Confirmed from the quotation in the Datawrapper post; original not read. Datawrapper post
John Burn-Murdoch’s tweet (lining up the colors outlets used in UK pay-gap reporting). Confirmed from the mention in the Datawrapper post; original tweet not read. Datawrapper post
Catherine D’Ignazio & Lauren F. Klein, Data Feminism, MIT Press, 2020, ch.4 “What Gets Counted Counts,” pp.111–113, fig.4.6–4.7, notes 48–50 (CC BY-NC-ND 4.0). Open-access edition at the Internet Archive
Sam Morris, Juweek Adolphe, Erum Salam, “Does the New Congress Reflect You?,” The Guardian, 2018 (comparison example in Data Feminism; not read by the author). Article
Cabric, Bjarnadóttir, Ling, Rafnsdóttir, Isenberg, “Eleven Years of Gender Data Visualization: A Step Towards More Inclusive Gender Representation,” IEEE TVCG 30(1):316–326, 2024. doi:10.1109/TVCG.2023.3327369 / arXiv

Overlaid at One Scale, Compared by Whether a Smartphone Fits: The Pudding's "Women's Pockets are Inferior"

Sat, 07 Nov 2026 00:00:00 +0900

The Pudding’s “Women’s Pockets are Inferior” (August 2018) measured the front pockets of 80 pairs of jeans from 20 US brands. From the measurements it drew outlines for men and women at the same scale and overlaid them, then recast the gap as whether a smartphone or other objects fit.

The authors are Jan Diehm and Amber Thomas. What follows reads the work in the order a reader meets the graphics, drawing on the project page, the Methods, and the measurement data, README, and drawing code published on GitHub. Some of the figures are set beside values that our researcher recalculated from the public data.

Summer 2018: Measuring a Complaint

The authors state their motive this way.

“like so many things on the internet, we could find complaints and anecdotes galore but little data illustrating just how inferior women’s pockets really are to men’s. So, we went there.”

In short: the internet is full of complaints and anecdotes but has almost no data, so they went and measured.

What they measured, according to the page and the data README, is as follows.

Item	Details
Sample	80 pairs from 20 popular US brands. Four per brand: men’s and women’s, in skinny and straight
Size	All waist 32 inches (per each brand’s size chart)
Measurements	5 front-pocket and 4 back-pocket dimensions (cm). Front pockets measured on the right side, empty
Period and places	June 29 to August 6, 2018. In-person stores in Nashville, New York, and Seattle
Published	Measurement data (CSV and JSON), measurement sheet (PDF), drawing code

The authors explain the choice of 32 inches by writing that “these jeans were all made to fit the same size person.” This answers the objection that men are bigger. The README also notes, however, that 32 inches is smaller than the median waist of US women. The reason given is that it is a size easily available to both men and women.

The limitations the authors list are these. There are only four pairs per brand. Some brands call skinny “slim,” and where there was no straight cut they used boot-cut. Where there was no standard or mid-rise, they substituted high-rise or low-rise. A sewn-shut fake pocket was replaced with another item of the same style. Buttons and zippers were opened and the pockets measured from the inside.

Stores and brands are described only as “popular.” It can be read that the page gives no criteria for choosing stores or brands, and says nothing about variability or significance testing.

Drawing Same-Scale Outlines from Five Measurements

The front pocket was measured in five lengths. The README’s diagram distinguishes the measured lengths, the lines drawn from them, and the computer-generated curves.

A diagram of front- and back-pocket outlines with measured points such as maxHFront and minWFront marked by red lines, and dashed curves distinguished in the legend as Computer-generated curves

Source: Jan Diehm, Amber Thomas / the-pudding/data (GitHub) (the repository is under the MIT License)

The authors state in Methods that the curves were not measured.

“While the overall height and width of the pockets was measured, the curvature of the pocket displayed in this article was computer generated.”

That is, height and width were measured, but the curves shown in the article were generated by computer.

The first graphic moves as you scroll. The outlines of all 80 pairs overlap as thin lines; then the average outlines for men and women appear side by side, and then overlap in the same position. The correspondences that can be read from the drawing code (bundle.js) are as follows.

Data	Appearance	Specifics
5 front-pocket dimensions (cm)	One closed outline per pair	Joined by straight lines and quadratic Bézier curves. The curves are computed
Height and width	Position and length	A common scale of 0 to 29 cm. The same scale for men and women
Each of the 80 pairs	Overlaid thin lines	Line opacity 0.1
Average by gender	Filled outlines	Translucent red-ish and yellow-ish
Object dimensions	Images at true proportions	iPhone X is 7×14 cm, for example
Fits or not	Opacity	Brands where it does not fit are shown faintly
Largest rectangle that fits the object	Image position and angle	Computed with d3plus’s largestRect()

Because men and women share one scale, the difference between the overlaid outlines is the difference in size.

“48% Shorter”: Shorter Than What?

The page sums up the front-pocket difference in one sentence.

“On average, the pockets in women’s jeans are 48% shorter and 6.5% narrower than men’s pockets.”

By style, it gives skinny as “3.5 inches (48%) shorter” and straight as “3.4 inches (46%) shorter.”

The formula for these percentages is written neither on the page nor in the README. So our researcher recalculated them from measurements.csv in the public data. Two percentages are given: the difference divided by the men’s average, and the difference divided by the average of the men’s and women’s means.

Front pocket	Page’s statement	Women’s mean	Men’s mean	Difference / men	Difference / mean of the two
Max height (all 80)	48%	14.33 cm	23.13 cm	38.1%	47.0%
Max height (skinny)	3.5 in (48%)	14.27 cm	23.18 cm	38.5%	47.6%
Max height (straight)	3.4 in (46%)	14.38 cm	23.07 cm	37.7%	46.4%
Max width (all 80)	6.5%	15.19 cm	16.37 cm	7.2%	7.5%
Max width (skinny)	0.3 in (6%)	15.07 cm	16.07 cm	6.2%	6.4%

(Our researcher’s calculation. Slim is included in skinny; boot-cut and regular are included in straight.)

The differences in inches match the 3.5 and 3.4 inches in the text. Taking men as the base gives about 38%, and women’s height is about 62% of men’s. Dividing the difference by the average of men and women gives 47 to 48%, which nearly matches the page’s figure. The authors seem to have used this formula, but they do not explain it. The 6.5% for width is closer to the skinny value than to the all-80 value (7.2 to 7.5%), and we could not confirm which aggregation it came from.

The same difference gives different numbers depending on the base. When a reader sees “48% shorter than men’s,” it can be read that they take it as a ratio with men as the base. On that ratio, the pocket is about 38% shorter, that is, about 60% of the length. Another case where the impression changes with the divisor is the country rankings in Green to Grey.

Does a Smartphone Fit?

The second half asks “What can actually fit?” When you pick an object, small diagrams of each brand’s front pocket line up by gender, with the object’s image placed inside. This is the structure of Small Multiples, which lines up identically shaped diagrams to show only the differences. There are seven objects: three smartphone models, a front-pocket wallet, a pen, a woman’s hand, and a man’s hand.

A diagram of three front-pocket outlines with a smartphone placed in them. In the left two, the smartphone sticks out sideways and is marked with a red X; in the right pocket it fits upright

Source: Jan Diehm, Amber Thomas / The Pudding “Women’s Pockets are Inferior” (the page’s social image)

Methods describes the test as follows.

“we used the largestRect() function from the d3plus library to determine the largest rectangle that could fit in each pocket that had the same aspect ratio (width:height) of the object in question.”

The procedure finds the largest rectangle that fits in the pocket with the same aspect ratio as the object. If the object is larger than that rectangle, or wider than the pocket opening, it is judged “does not fit.” For hands, it counted as fitting if the four fingers other than the thumb went in to the base. The test was done on empty pockets, not while worn.

The page summarizes the result this way.

“Only 40 percent of women’s front pockets can completely fit one of the three leading smartphone brands.”

It also says that a front-pocket wallet fits in fewer than half.

The original says only “one of the three leading smartphone brands,” with no per-model values. Our researcher redid the judgment using the largest rectangles in measurementRectangles.json and the width of the pocket opening, with the following results.

Object (dimensions)	Women’s front pockets	Men’s front pockets
iPhone X (7×14 cm)	40%	100%
Samsung Galaxy (6.9×14.7 cm)	20%	95%
Google Pixel (7.6×15.7 cm)	5%	85%
Woman’s hand (7×12 cm)	10%	100%

(Our researcher’s calculation. The JSON has no cm values for the wallet, so it could not be rechecked.)

The page’s 40% matches the iPhone X value. The 10% for the hand also agrees with what Diehm said on a New Zealand program in 2020: “Only 10 per cent can fit the average women’s hand.”

How Third Parties Saw the Work

Each assessment points to a different aspect. The first is an award. At the Kantar Information is Beautiful Awards 2018, it received JOINT BRONZE in the People, Language & Identity category. The blurb is the same as the authors’ summary: “48% shorter and 6.5% narrower.”

The second is its treatment in a theory book. Catherine D’Ignazio and Lauren F. Klein’s Data Feminism (2020) takes up the work in Chapter 4, pp. 107–108.

“Now, the designers of any particular pair of women’s jeans are almost certainly not thinking: ‘Let’s oppress women by making their pockets too small.’ They are probably only thinking about what looks nice. But what looks nice has a history too.”

The point is that the designers are not trying to oppress women, but “what looks nice” has a history too. It can be read that the book uses the work as an example of inequality appearing in the dimensions of everyday objects even without ill intent. The book also writes “40 percent … can fit a smartphone,” dropping the condition “one of the three brands.”

The third is a statistics-education perspective. In a 2021 blog post, Jessica Hartnett praised the release of the data and recommended it as material for teaching the t-test.

“Honestly, that is good science and good transparency.”

That is, frankly, good science and good transparency. However, the sources disagree on who did the measuring.

Source	Description of who measured
Data README	“manual measurements by Jan Diehm and Amber Thomas” (the two authors measured)
Hartnett’s blog (2021)	“sent research assistants to measure the pockets” (assistants measured)

We could not confirm which is correct.

In press coverage, the figures were reworded. The Daily Beast (September 2, 2018) introduced it as an “informal study” while writing that the pockets were “just half the size.” “48% shorter” has become “half the size.” We found no article that criticized the method head-on.

Takeaways

There are four judgments to carry back to visualization work.

The first is to overlay the two things being compared at the same scale. This work drew a sample matched at 32 inches on a single scale. Overlaying after conditions are matched lets the difference show as a shape.

The second is to recast the difference as something the reader carries. “Fits an iPhone X or not” can be checked against your own pocket in a way that “how many cm shorter” cannot. The procedure of finding the largest rectangle with the object’s aspect ratio can be used for other “does it fit” comparisons.

The third is to state the base of a percentage. By our researcher’s calculation, dividing the difference in maximum height by the men’s value gives about 38%. The authors’ 48% is close to the value divided by the average of men and women, and the formula is not explained. When the formula is not written, it can be read that the figure is easily taken as a ratio to men.

The fourth is to separate what was measured from what was made, and to release the data. The README diagram distinguished computer-generated curves in its legend. The recalculation in this article was also possible only because the CSV and JSON were public. It can be read that the readers’ ability to check for themselves when a number raises doubts is what supports the claim.

References

Primary sources

Jan Diehm & Amber Thomas, “Women’s Pockets are Inferior,” The Pudding, 2018-08. Article
Drawing code of the same page (bundle.js). JS
Data of the same page. measurements.csv / measurementsRectangles.json
Measurement sheet (MeasurementGuide.pdf). PDF
the-pudding/data, pockets (README, CSV, JSON). GitHub / README
License of the-pudding/data (MIT License). LICENSE
the-pudding/pockets (source code). GitHub / rect-calc-template.js
d3plus, largestRect. Documentation

Secondary sources

Catherine D’Ignazio & Lauren F. Klein, Data Feminism, MIT Press, 2020. Chapter 4, pp. 107–108, Figure 4.4. Open-access edition / Full text
Kantar Information is Beautiful Awards 2018 winners list. Article / Showcase
Jessica Hartnett, “Women’s pockets are crap: An empirical investigation,” Not Awful and Boring, 2021-06. Blog
Victoria Albert, “Science Proves Women’s Pants Pockets Suck,” The Daily Beast, 2018-09-02. Article
“Fair Go investigates: Are women’s pants pockets really smaller then men’s?,” 1News, 2020-03-16. Article

Waiting for the First Woman's Name: Reading a Scene from A Sort of Joy

Sat, 07 Nov 2026 00:00:00 +0900

A Sort of Joy (Thousands of Exhausted Things) is a collaboration between the theater company Elevator Repair Service (ERS) and The Office for Creative Research (OCR). Its script was generated from the Museum of Modern Art (MoMA) collection data (123,951 works), and it was performed in April 2015 in the second-floor galleries of MoMA. Data Feminism describes one scene from this performance: the first names of artists are read out in order, with male performers voicing male names and female performers voicing female names. It takes more than three minutes to reach the first female name, “Mary,” and the authors argue that this wait is an experience of feeling a bias rather than seeing it. This description could not be confirmed in the primary sources.

The performance was one of the Artists Experiment projects run by MoMA’s education department. What follows draws on the official descriptions from OCR and Vimeo, an interview with the director, and Chapter 3, pp. 85–87, of Data Feminism by Catherine D’Ignazio and Lauren F. Klein (MIT Press, 2020). Data Feminism is the only source that describes the names scene, and it differs from the primary sources on three points, which are set side by side below.

April 2015: performing collection data on MoMA’s second floor

The performance took place on April 4 and 7, 2015, in MoMA’s second-floor contemporary galleries, during regular opening hours (Visionaire World). According to the Vimeo credits, it was directed by John Collins, founder of ERS. The concept was by Collins and the four OCR members Mark Hansen, Ben Rubin, and Jer Thorp, with six performers.

OCR’s project page describes the form of the performance this way:

“… a variable performance script that is delivered to actors electronically as they roam throughout the second floor galleries of the museum. … this spoken text and the ambient sound that accompanies it is experienced through wireless stereo headphones worn by audience members.”

In other words, a script made from the 123,951 works in the collection data was delivered electronically to actors roaming the second-floor galleries, and the audience listened to the voices and ambient sound through headphones. The same text calls the performance an “extended audio tour.”

Headphone-wearing people stand scattered in a white gallery with artworks hanging from the ceiling; a man in a vest at the center holds a tablet in front of his chest

Each run lasted about 45 minutes and was repeated twice within the scheduled time, and the audience was free to come and go. The script was generated for each run (EAR Studio). The official description states that every word spoken was taken from the database.

Replacing the wall label with voices and order

The data used was each work’s title, artist name, medium, dimensions, year of production, and so on. In an interview with Visionaire World, Collins explains that this is the information printed on the small card next to a work (the tombstone).

According to the Vimeo description, the work titles were classified by grammar, vocabulary, stress, and so on, and rules for sorting and filtering were applied to generate sequences of sentences. Some of these sentences were delivered as they were, and others were put together by the artists by hand.

An iPad held in a hand; below the symbols T, A, Y, M, D, and N, the screen lists in a column the work title, artist name, year, medium, dimensions, and nationality with birth year

On the screen in the photo, under work titles such as “Cantiere Barca” and “The Fall of a Hair: Blow Ups,” the artist name, year, medium, dimensions, and nationality are lined up. The fields of the wall label appear to have become the lines of the script.

The table below summarizes, source by source, what data was mapped to what. The first four rows are Data Feminism’s description of the names scene, which could not be confirmed in the primary sources.

Data	Mapped to	Source
Artists’ first names	The words spoken aloud. The iPad shows the full names of artists with that first name	Data Feminism p. 86 (not confirmed in primary sources)
Size of each group of names	The order of reading. Larger names come first (the criterion is examined in a later section)	Same, p. 86 (not confirmed in primary sources)
Gender of the name	Who speaks. Male performers for male names, female performers for female names	Same, pp. 86–87 (not confirmed in primary sources)
The gap between male and female names	How long the female performers walk in silence. More than three minutes until “Mary”	Same, p. 87 (not confirmed in primary sources)
Work titles	Chains of association linked by words (“boy” → “boy holding shovel” …)	Visionaire World (Collins)
Medium	A list of medium names read aloud for works in the same medium	Visionaire World (Collins)
Syllables and stress of artist and medium names	Fitted to the melody of a familiar song (e.g., “My Favorite Things”)	Visionaire World (Collins)
The volume of the whole dataset	Many scenes sounding at once, making “the voice of the whole museum”	Visionaire World (Collins)

The names scene as Data Feminism describes it

In the section “Data Visceralization” in Chapter 3, Data Feminism takes up this performance in connection with a feminist critique of museum collecting policy. It places the piece alongside the Guerrilla Girls’ 1989 poster as another tactic with the same aim.

In the authors’ description, a group of white men stands in a circle in the middle of the room, facing outward toward the audience. When the men say “John” in unison, the iPads show John Baldessari, John Cage, and so on. Three female performers keep walking silently around the circle. Male names follow, “Robert” and “David,” and eventually “Mary” comes as the first voice of the women.

“It takes more than three minutes for “Mary” to be spoken, and the next female name, “Joan,” doesn’t come for a full minute longer.”

The description says it took more than three minutes to reach Mary, and a full minute more to reach the next female name, Joan. “Barbara” follows soon after, and then the names return to male ones.

The authors also write that, as analysis, this is simple.

“From a data analysis perspective, A Sort of Joy consists of simple operations: counting and grouping. A bar chart or a tree map of first names could easily have represented the same results. But presenting the dataset as a time-based experience makes the audience wait and listen and experience.”

Their claim is that a bar chart could show the same result, but placing it in time makes the audience wait, listen, and experience. The authors call this the opposite of Ben Shneiderman’s “Overview first”: the listener hears one point at a time and builds up the whole gradually.

“We start to anticipate when the next woman’s name will arise. We feel the gender differential, rather than see it.”

The line says that we start waiting for the next woman’s name, and feel the difference rather than see it. The authors call this feeling affect, and include even the urge to leave for another room because it goes on too long.

Three points where Data Feminism and the primary sources differ

Data Feminism’s description and the primary sources differ on the following three points.

Point	Data Feminism	Primary sources
How the names are ordered (whether it is described)	In the order of “which first name has the most works in the collection.” John, Robert, and David come first because they have the most works (p. 86)	No description in the sources I could read (OCR, Vimeo, EAR, Visionaire). Thorp’s Medium post was not read
Description of the performance	The audience surrounds a circle of men in the middle of the room, and three women walk around it. The performers wear the headphones (p. 86)	Six performers roam the second-floor galleries in an “audio tour.” The audience wears the headphones (OCR, Vimeo)
Video	Note 30 says “the full performance” can be seen on Vimeo	The Vimeo title is “A Sort of Joy at MoMA (excerpts).” The description opens with “[ ROUGH CUT ],” and the running time is 8 min 04 s

The two descriptions of the performance can also be read as compatible, if the names scene was one scene within a performance that moved through the galleries. In the photos, the people holding tablets also appear to be wearing headphones. However, where in the 45 minutes the names scene fell and who took part could not be confirmed in the primary sources.

In a white gallery, several people wearing headphones stand in a row looking at tablets in their hands

The ordering of the names can be recounted with today’s data. The numbers below are not the makers’; they were counted for this article. The data used is MoMA’s GitHub data as of November 2026 (160,928 works, 15,999 artists). It was published on GitHub in July 2015, after the performance. Cuseum writes that the performance used “an earlier version of the dataset.” It is not the version from the 2015 performance.

Ordering criterion	Top names	Position of female names
Number of artists with the name	John 254, Robert 197, David 152, Paul 141, William 124 …	Mary is 46th (33 artists). Barbara is 54th, Maria 65th, Joan 66th
Number of works by artists with the name	Ludwig 16,406 works, Eugène, Louise, Robert, John …	Louise is 3rd. Mary has 91 works, far lower

Ordered by number of works, Ludwig comes first, because of the Mies van der Rohe architecture archive the museum holds. The order by number of artists matches the John, Robert, David sequence in Data Feminism well. This suggests that the actual criterion was probably the number of artists with the name. Note that the ranking by artist count also includes words such as “The” that are not names, and how the gender of each name was determined is not known.

How other writers saw the performance

In 2017, Mindy Seu cited the performance as an example of digital poetry in “The Poetry of Tools” on Are.na Editorial. She does not mention gender.

“Removed from the context of the artwork, the performance focused on the data itself and reshaped how it can be consumed.”

Her assessment is that it turned attention to the data itself, away from the artworks, and reshaped how the data is received. Seu also writes that OCR turned the data into a script after its publication on GitHub, but the script must have been made before the April performance, so the order appears to be reversed.

Pablo Helguera, who was then in MoMA’s education department, cited it in 2021 as an example of practice that uses artwork titles as material.

“The piece was a combination of conceptual poetry, theater of the absurd, Kurt Schwitter-esque verbal experiments and musical delight”

He describes it as a mixture of conceptual poetry, theater of the absurd, Schwitters-style verbal experiments, and musical delight.

In “A sort of joy” (2020), Zana Fauzi widened the question to race and intersectionality.

“We never heard a ‘Khalil’, a ‘Salama’, a ‘Dinesh’ …”

The point is that no Khalil, Salama, or Dinesh was heard.

Takeaways

There are four judgments to take back to visualization work.

The first is to turn quantity into time. In Data Feminism’s description, a difference in numbers that a bar chart could show becomes waiting time when names are read one at a time. Like Two Trains, which turned the distance between stations into the length of a song, this can be read as an example of using time as an axis.

The second is to assign a classification to “who speaks.” In Data Feminism’s description, the gender of a name is carried by the speaker’s voice and body rather than by the content of the words. This is a different place to put it from Quotidian Record, which maps the frequency of visited places to consonant and dissonant intervals, or Chemo Singing Bowl, which maps it to the shape of an object.

The third is the cost of showing the whole at the end. The listener is kept waiting for a long time. Data Feminism argues that affect includes even the urge to leave for another room because it goes on too long. Whether to show the whole first comes down to whether you want people to feel it or to read off the values.

The fourth is to write down the ordering criterion. Whether the order is by number of artists or number of works, the first name could be John or Ludwig. A work known from secondary sources also has to be checked again against the primary sources. In this performance, the introductions and the records differed on three points: the ordering of the names, the setup of the space, and the video.

References

Primary sources

The Office for Creative Research, “A Sort of Joy (Thousands of Exhausted Things),” 2015. Project page
Ben Rubin, “A Sort of Joy at MoMA (excerpts),” Vimeo, 2015-07-17 (8 min 04 s). Video / oEmbed (description)
EAR Studio (Ben Rubin), “A Sort of Joy (Thousands of Exhausted Things).” Project page
Jer Thorp, “A Sort of Joy.” Project page
Alessandro Magania, “Elevator Repair Service’s Sort of Joy” (interview with John Collins), Visionaire World. Article
The Museum of Modern Art, “The Museum of Modern Art (MoMA) Collection,” GitHub (CC0). Repository

Secondary sources

Catherine D’Ignazio and Lauren F. Klein, Data Feminism, MIT Press, 2020, ch.3, pp.85–87, 96, notes 30, 34, 35. Internet Archive
Mindy Seu, “The Poetry of Tools,” Are.na Editorial, 2017-08-10. Article
Pablo Helguera, “Untitled (On Titles),” Beautiful Eccentrics, 2021-12-09. Article
Zana Fauzi, “A sort of joy,” Two Kinds of Intelligence, 2020-09-27. Article
Cuseum, “MoMA and the exciting possibilities with museum metadata,” 2015-09-25. Article
Quim Gil, “The MoMa, CSV, GitHub, and CC0,” Wikidata mailing list, 2015-07-29. Post