Size Matters to Lesbians, Too: Queer Feminist Interventions into the Scale of Big Data

TLDR: In this paper, the authors examine the place of lesbians and queer women in the big data debates through the Lesbian Herstory Archive's not "big" enough lesbian, gay, bisexual, trans, and queer (LGBTQ) organizing history data set.

Abstract: How can we recognize those whose lives and data become attached to the far-from-groundbreaking framework of “small data”? Specifically, how can marginalized people who do not have the resources to produce, self-categorize, analyze, or store “big data” claim their place in the big data debates? I examine the place of lesbians and queer women in the big data debates through the Lesbian Herstory Archive's not “big” enough lesbian, gay, bisexual, trans, and queer (LGBTQ) organizing history data set—perhaps the largest data set known to exist on LGBTQ activist history—as one such alternative. In a contribution to critical data studies, I take a queer feminist approach to the scale of big data by reading for the imbricated scales and situated knowledge of data.

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Do not quote, excerpt, or reprint without written permission of the author. Instead cite:
Gieseking, J. 2017 (forthcoming). “Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data.” Professional
Geographer.
Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data
Jen Jack Gieseking
Assistant Professor of Public Humanities
American Studies, Trinity College, Hartford, CT USA
ABSTRACT
How can we recognize those whose lives and data become attached to the far-from-
groundbreaking framework of “small data”? Specifically, how can marginalized people who
do not have the resources to produce, self-categorize, analyze, or store “big data” claim their
place in the big data debates? I examine the place of lesbians and queer women in the big
data debates through the Lesbian Herstory Archive’s not “big” enough lgbtq organizing
history dataset—perhaps the largest dataset known to exist on lgbtq activist history—as one
such alternative. A contribution to critical data studies, I take a queer feminist approach to
the scale of big data by reading for the imbricated scales and situated knowledge of data.

Do not quote, excerpt, or reprint without written permission of the author. Instead cite:
Gieseking, J. 2016. “Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data.” Professional Geographer.
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Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data
Jen Jack Gieseking
Assistant Professor of Public Humanities
American Studies, Trinity College, Hartford, CT (USA)
Alice Pieszecki (Leisha Hailey) claims that Los Angeles lesbians, bisexuals, and
queers form a closely-knit network through sexual encounters and relationships. She
draws a diagram to prove this to her friend Dana Fairbanks (Erin Daniels), another
lesbian, who is shocked by the interrelated intimacy.
Dana: It’s like this whole crazy, tiny, little world.
Alice: Crazy, yes. [Pauses.] But not tiny.
The camera pans up to a larger 6’ x 3’ hand-drawn chart on Alice’s wall with over
100 women’s names connected by lines. (“The L Word,” Season 1, “Pilot (Pt 2),”
2004)
Alice’s obsession with rendering the “not tiny” world of L.A. lesbians in “The L Word”
demonstrates the labor necessary for lesbians and queers to confront their invisibilization: a
requirement to constantly produce accumulated evidence of their lives, experiences, and
spaces. Yet even before the first and, still, only (premium cable) network show about
lesbians premiered in 2004, “more data were accumulated in 2002 than all previous years
of human history combined” (Bail 2014, 465). Can “not tiny”-but-big-for-its-context data ever
qualify as big enough? In this paper I address how size (of data) matters to lesbians too. My
joke and, in actuality, insight reveals how the objective and scientific claims of big data gain
validity through the measuring stick of masculinist, racist, and heteronormative structural

Do not quote, excerpt, or reprint without written permission of the author. Instead cite:
Gieseking, J. 2016. “Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data.” Professional Geographer.
!
2
oppressions. Society’s obsession with big data further oppresses the marginalized by
creating a false norm to which they are never able to “measure up.” A contribution to critical
data studies, I take a queer feminist approach to the scale of big data by reading for the
fluidity and situated knowledge of datasets.
My interest in how scale plays a role in the production of big data emerged from my
years of archival research at the Lesbian Herstory Archives (LHA or Archives). Most data
collected about lesbian, gay, bisexual, trans, and queer (lgbtq) people throughout history has
been used to pathologize and stigmatize. Lgbtq people and women could and sometimes
still can leave few if any records of their lives. The LHA, in Park Slope, Brooklyn, New York—
founded in 1974 by, for, and about lesbians to record their own history—is the largest and
oldest lesbian archive in the world. In a time when bigger data is better data, I take up my
work with the largest or one of the largest existing lgbtq record collections in existence. New
York City is a particular world hub of lgbtq organizing and the outcomes of these data and
my analyses provide groundbreaking understandings into lgbtq history in the city and
beyond, as I will show.
However, detailed notes on all 382 NYC-based organizations during my period of
study amount to, in the eyes of big data, a mere 789KB worth of data. If lgbtq activists,
historians, scholars, and leaders and other marginalized groups cannot claim their data to
be “big data,” are they disavowed from claiming the equally large arguments or
understandings big data is said to provide with their “small data”? Instead of simply
rejecting the politics of scale in data’s big-ness, I refute the big-small data binary to show
how lesbian-queer data works in interdependent scales.
1
I suggest instead that big data
must be sized up through its mythos, measurements, and the pace of its accumulation.

Do not quote, excerpt, or reprint without written permission of the author. Instead cite:
Gieseking, J. 2016. “Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data.” Professional Geographer.
!
3
I apply a queer feminist approach to critical data studies, as it requires an
acknowledgement of the absences in data as well as dimensions of power of who can form
and define data. Rather than submit to the suggestion that the LHA organization collection
data is “small data” and by extension less in measure and import than “big data,” I offer
ways to recognize the already marginalized and less studied lives, experiences, spaces, and
histories of the oppressed in the moment of big data, including the poor, people of color,
colonized, disabled, and lgbtq people. Instead, I suggest that new insights can be gained by
accounting for multiple, nested, and imbricated scales of data.
Queer Feminist Critical Data Studies: On Scale & Big-Small Data
Drawing upon a queer feminist and critical geographic perspective, I argue that the scale of
data must be read within the context and time in which it is and can be produced. Queer
feminism affords me a way to intervene on behalf those who are invisibilized by hegemonic
practices of data collection, analysis, and visualization.
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Instead there exist a wide range of
imbricated scales of data which upend the big-small data binary.
Manifold definitions of “big data” abound. I provide a framework for big data through
the work of Rob Kitchin and danah boyd and Kate Crawford. Taking a primarily technical
approach to the definition, Kitchin (2014) identifies seven features of big data: huge in
volume, high in velocity, diverse in variety, exhaustive in scope, fine-grained in resolution,
relational in nature, and flexible in both its extensionality and scalability. Kitchin absorbs
boyd & Crawford’s (2012) take on big data as a socio-technical phenomenon into his own
schema. However, boyd and Crawford also point to big data’s mythos, its least often
addressed trait. The mythos, they suggest, is that “large data sets offer a higher form of
intelligence and knowledge that can generate insights that were previously impossible, with

Do not quote, excerpt, or reprint without written permission of the author. Instead cite:
Gieseking, J. 2016. “Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data.” Professional Geographer.
!
4
the aura of truth, objectivity, and accuracy” (boyd and Crawford 2012, 663). The mythos
characteristic remains underexplored by Kitchin and is the lynchpin to my arguments here.
“Small data” are then defined as an antithetical complement to big data. Small data
are “characterized by their generally limited volume, non-continuous collection, narrow
variety, and are usually generated to answer specific questions” (Kitchin and Lauriault
2015, 1). While small data are “popular and valuable” in “their utility in answering targeted
queries,” Kitchin and Lauriault state that small data cannot contend with the infrastructures
or related/afforded analytics of real-time, indexical, and relational data (2015, 1). This
binary conceptualization fails to address the meaning and mythos within by relying purely on
literal technicalities to define big data.
Just as space is given meaning and power in the production of various scales of
space from the global to the intimate, so is data from big to small. Politics, positionality, and
power of data and spaces alike can be exposed by through the geographic concept of scale
(see Marston 2000). Scale is socially constructed through political and economic processes
that contribute to the processes of geographical uneven development. Geographer
Geraldine Pratt and literary theorist Victoria Rosner (2012) reimagined scale through an
interdisciplinary feminist lens to show how scales permeate and are nested within one
another. The authors refuse the local-global binary and its parallel feminine-masculine
pairing, and instead call for an examination of “the global and the intimate.” Intimate
relations are simultaneously global and local, just as the global is experienced in and
through the intimate and all the scales in between.
Pratt and Rosner’s contribution to the scale literature is also, I suggest, a queer
feminist approach. Unlike local-global or small-big dyads, the global and the intimate respect
feminist situated knowledges while being held in a queer tension that refutes binaries and

Frequently asked questions

Q1. What contributions have the authors mentioned in the paper "Size matters to lesbians too: queer feminist interventions into the scale of big data" ?

Specifically, how can marginalized people who do not have the resources to produce, self-categorize, analyze, or store “ big data ” claim their place in the big data debates ? Do not quote, excerpt, or reprint without written permission of the author. 1 Size Matters to Lesbians Too: Queer Feminist Interventions into the Scale of Big Data Jen Jack Gieseking Assistant Professor of Public Humanities American Studies, Trinity College, Hartford, CT ( USA ) Alice Pieszecki ( Leisha Hailey ) claims that Los Angeles lesbians, bisexuals, and queers form a closely-knit network through sexual encounters and relationships. In this paper I address how size ( of data ) matters to lesbians too. My joke and, in actuality, insight reveals how the objective and scientific claims of big data gain validity through the measuring stick of masculinist, racist, and heteronormative structural Do not quote, excerpt, or reprint without written permission of the author. New York City is a particular world hub of lgbtq organizing and the outcomes of these data and my analyses provide groundbreaking understandings into lgbtq history in the city and beyond, as I will show. If lgbtq activists, historians, scholars, and leaders and other marginalized groups can not claim their data to be “ big data, ” are they disavowed from claiming the equally large arguments or understandings big data is said to provide with their “ small data ” ? Do not quote, excerpt, or reprint without written permission of the author. Rather than submit to the suggestion that the LHA organization collection data is “ small data ” and by extension less in measure and import than “ big data, ” I offer ways to recognize the already marginalized and less studied lives, experiences, spaces, and histories of the oppressed in the moment of big data, including the poor, people of color, colonized, disabled, and lgbtq people. The mythos, they suggest, is that “ large data sets offer a higher form of intelligence and knowledge that can generate insights that were previously impossible, with Do not quote, excerpt, or reprint without written permission of the author. The authors refuse the local-global binary and its parallel feminine-masculine pairing, and instead call for an examination of “ the global and the intimate. Unlike local-global or small-big dyads, the global and the intimate respect feminist situated knowledges while being held in a queer tension that refutes binaries and Do not quote, excerpt, or reprint without written permission of the author. Do not quote, excerpt, or reprint without written permission of the author. Do not quote, excerpt, or reprint without written permission of the author. Along with years of research in the Lesbian Herstory Archives in Brooklyn, New York, my project included group interviews with 47 self-identified lesbians and queer women who came out during this period. This two-pronged approach of collecting primary and secondary research provides for a vivid political and socioeconomic backdrop for the group conversations. The LHA data shows, as Trevor Barnes writes, that big data “ is presented as if it were disconnected from the past, removed from issues or problems that went before ” ( Barnes 2013, 297 ). While the LHA is hard at work on their own digitization ( McKinney 2014 ), in Do not quote, excerpt, or reprint without written permission of the author. 8 this paper I am particularly interested in subsets or collections of data that have a stake in but are unaddressed in the big data conversations. I have written about the difficult project of acquisition of lgbtq archival materials that primarily focus on women and trans * people ( see Gieseking 2015 ). 5 I turned to the only two LHA collections ( of 17 ) with dates and locations with consistently recorded locations and dates in order to ground them to my period and place of study. Only the organization and Do not quote, excerpt, or reprint without written permission of the author. 9 publications collections fit my location-date requirements, and I found these documents enormously insightful regarding lesbian-queer political economies ; and I focus on the organizational records alone in this paper. Do not quote, excerpt, or reprint without written permission of the author. Reading and categorizing the Do not quote, excerpt, or reprint without written permission of the author. The brunt of the work was completed in multiple trips per week in just over a year, followed by multiple return trips over the following five years as well as the employing of research assistants. I have saved the best for last: while New York City is a particular world hub of lgbtq organizing, detailed notes on all 382 NYC-based organizations during my period of study amount to a mere 789KB worth of data in. Do not quote, excerpt, or reprint without written permission of the author. Discussion: Interrupting the Mythos of Big-Small Data “ Big data ” claims its authority in its largess and seeming totality. Different activist and community groups make their own data from the ground up to lay their claim in big data ( see Taylor et al. 2014 ; Dalton 2017 ). Do not quote, excerpt, or reprint without written permission of the author. Do not quote, excerpt, or reprint without written permission of the author. 14 Returning to the hand-drawn social network analysis of lesbian-queer relationships of “ The L Word ” that I began this paper with, Alice later exclaims about the chart: “ So the point is that the authors ’ re all connected, see ! To be truly critical in the project of data studies requires both the recognition of the unique standpoints of feminist, queer, critical race, postcolonial, and crip theory and the groups they speak alongside ( see Cupples 2015 ). The stories and the data of the Do not quote, excerpt, or reprint without written permission of the author. My eternal appreciation to Megan L. Cook, and issue editors Ryan Burns, Craig Dalton, and Jim Thatcher for comments on this paper as well as their adventurous academic friendship together. This research was supported by the following funding for which I remain deeply grateful: Woodrow Wilson Dissertation Fellowship in Women ’ s Studies ; Center for Place, Culture, and Politics ; Joan Heller-Diane Bernard Fellowship from the CLAGS ; and the CUNY Graduate Center Proshansky Dissertation Award. Society ’ s obsession with big data further oppresses the marginalized by creating a false norm to which they are never able to “ measure up. ” A contribution to critical data studies, I take a queer feminist approach to the scale of big data by reading for the fluidity and situated knowledge of datasets. 1 I suggest instead that big data must be sized up through its mythos, measurements, and the pace of its accumulation. Instead, I suggest that new insights can be gained by accounting for multiple, nested, and imbricated scales of data. Pratt and Rosner ’ s contribution to the scale literature is also, I suggest, a queer feminist approach. Furthermore, Craig Dalton and Jim Thatcher ( 2014 ) stipulate that “ data has always been big ” and “ big isn ’ t everything ” as big data can not answer all research questions, just as studies of the global can not alone define everyday intimacies and vice versa. I do not suggest that all data need be “ big data. ” I do seek to reveal how the binary of big-small data reproduces heteronormative, patriarchal, and racist oppressions in who it leaves out or puts down. Collecting, organizing, and maintaining these materials required further labor, as well as the significant funds necessary to maintain an archive. Yet would anyone ever suggest this dataset is small Further, the authors must take up quantitative data analytic models that work beyond absolute, Cartesian models of space that fix identities as such ( O ’ Sulliivan, Bergmann, and Thatcher 2017 ). I suggest that society ’ s obsession with big data further oppresses the marginalized by creating a false norm to which they are never able to measure up. 

Q2. What is the role of gender in data studies?

A queer feminist approach to data studies exists interdependently with othersubjectivities such as race, class, gender, age, and ability. 

Q3. How many years of experience did the Lesbian Herstory Archives have?

In total, the 382 organizations were open for a period of over 3,108 years of experience, thereby producing at least 3,108 distinct organizational events over the years and likely in the hundreds of thousands. 

Q4. What was the labor required to organize and sort these organizations?

Recording and sorting these organizations and their related records— fliers, newsletters, lists of members, photos from activist interventions, meeting minutes, banners from marches, and even a few balance sheets—required a great deal of labor, on a scale only possible with the help of various grants and fellowships. 

Q5. What is the role of the critique in data studies?

In step with this critique and also a contribution to critical data studies, states and corporations also reproduce heteronormative, sexist, able-ist, and ageist policies of surveillance and regulation through the assembly and examination of data (see Leszczynski 2015). 

Q6. How many trips did the author take to organize the documents?

The brunt of the work was completed in multiple trips per week in just over a year,followed by multiple return trips over the following five years as well as the employing of research assistants. 

Q7. What did The authordo to find the key events of the groups?

I sought out the key social, political, and economic events of the groups that allowed me to tap into the events, spaces, and people that defined each year of my study. 

Q8. How did The authorget the data to be able to read?

Yet to turn this data into information and knowledge, it was necessary to transfer what The authorestimate to be about 30 linear feet of materials into a format that The authorcould search, sort, collate, and piece together to read the everyday landscape of lesbian-queer New York City. 

Q9. What does the author seek to reveal about the binary of big-small data?

I do seek to reveal how the binary of big-small data reproduces heteronormative, patriarchal, and racist oppressions in who it leaves out or puts down.