This final chapter is a toolkit geared towards anyone who may be contemplating starting or joining a counterdata science project. Perhaps you are a data activist or a journalist, an urban planner or an academic, a head of a nonprofit or a member of a community group. Or somebody else entirely. My hope is that these pages will offer both practical guidance and seeds of contemplation.
As I sat down to map the contours of this toolkit, I realized again and again that all of the ideas I thought I had were actually things that activists had told me, or showed me, or taught me. One of the most humbling and inspiring aspects of writing this whole book has been the continual realization of the depth and the breadth of what our partners have to teach the rest of us who aspire towards transformative social change. They center care, memory and justice without sacrificing rigor. They don't imagine data as a "solution" but rather see data as one tactic in a larger, networked movement of social and political actors. They stay close to the communities and families impacted by feminicide, in some cases providing direct healing and support. They are highly creative in acquiring information from a deeply biased, unjust information ecosystem. They develop ways to circulate feminicide data for diverse impacts, ranging from policy reform to media reframing to mass mobilizations.
This toolkit is a first step towards drawing out some of these lessons and speculating how these may be useful to anyone using data in the service of social justice. First, it includes a glossary of some key terms. Then, it introduces the idea of data epistemologies and situates data feminism as one approach, amidst other possibilities. There is a section with examples of projects that showcase an assortment of counterdata science models for inspiration in domains beyond gender-based violence. Finally, the toolkit provides a list of questions to ask or short activities to engage in at the different stages of a counterdata science project (resolving, researching, recording, and refusing and using data). At each stage, the toolkit questions and activities are grouped by the data feminism principle they that they resonate with.
The questions and activities in the toolkit are drawn from themes that surfaced in our work on feminicide, but they are purposely written to be broadly applicable. My hope is that these reflective questions and activities will be useful to anyone using data-driven methods for monitoring, auditing and inquiry, and especially projects related to structural inequality. I am confident that at least some of the many lessons learned from counting feminicide will be helpful for other efforts working to count and document human rights violations and expose them as structural problems. These might include, for example, the already existing efforts to document police killings of Black Americans, LGBTQIA+ hate crimes, or the murders of Indigenous land defenders.
There are also counterdata efforts occurring not only related to physical violence but other forms of economic violence or structural inequality. Recently there has been a remarkable growth, for example, in nonprofit and activist groups that count and monitor evictions in the United States1. Here the rows of data represent cases of eviction rather than cases of feminicide, but the intent is similar: to use quantification to clarify connections between eviction and settler colonial dispossession, eviction and anti-Black racism, eviction and health equity, eviction and state violence. Here there is great resonance with the anti-feminicide projects' examination of power and use of data to reframe and remake personal problems into structural patterns. In order to draw connections between anti-feminicide data activism and work in other domains, I have included a list of counterdata science examples in the toolkit.
That said, because the toolkit arose in the context of anti-feminicide data activism, it is highly likely that not all questions apply to all counterdata science projects in all domains. And there are most certainly questions and concerns that arise in specific domains, such as eviction monitoring, which will not surface here. I encourage people to use what is useful and, if you are moved to do so, contribute comments, questions, and critiques to the evolving open toolkit located on PubPub (here!).
Co-liberation – This is the goal of working towards social justice; that we, together, can free ourselves of the multiple burdens – material, psychic, spiritual and intergenerational – of systemic oppression. There is a well-known quote from aboriginal activists in Queensland, Australia, that best represents this idea: “If you have come here to help me you are wasting your time, but if you have come because your liberation is bound up with mine, then let us work together.”2
Counterdata – Data that are produced by civil society groups or individuals in order to counter missing data or to challenge existing official data. This could be through contesting official definitions, official measurement practices, or official analysis practices. Producing counterdata isn't only about filling in the gaps of official data, but is also used to challenge state bias and inaction, to galvanize media and public attention, to work towards policy change and to help heal wounded communities.
Counterdata science – An explicit, systematized, rigorous, and usually collective, challenge to the data practices (measurement, collection, analysis, publication) of mainstream "counting institutions" such as governments and corporations. Appropriates conventional data science practices and uses them to produce knowledge about a phenomena from outside the official counting institutions. Counterdata science is not only countering official data (or lack thereof) but also countering hegemonic data science by enacting an alternative vision of what data science is, who does it, and who it benefits. Counterdata science is a citizenship practice – a way of using data and measurement to enact democratic dissent about data and measurement.
Data activism – The use of data and software to pursue collective action and exercise political agency. Producing counterdata and engaging in counterdata science are specific ways – but far from the only ways – of engaging in data activism.
Data epistemologies – Theories and approaches to knowing things about the world with data. Mainstream data epistemologies are heavily positivist – seeking to use data to find universal truths over a consideration of context. Many scholars and activists have highlighted how mainstream data epistemologies replicate violent and extractive and colonial modes of knowledge generation. Emerging alternative data epistemologies include data feminism, feminist data refusal, radical data science, decolonial AI, Indigenous data sovereignty, and queer data.
Discordant data – An idea from Helena Suárez Val that describes the fact that official data and counterdata often deliberately do not coincide – they are discordant because they use different definitions, measurement and classification strategies3.
Hegemonic data science – Mainstream data science which works to concentrate wealth and power; to accelerate racial capitalism, perpetuate patriarchy, sustain settler colonialism; and to exacerbate environmental excesses and social inequality.
Missing data – Data that are neglected by institutions, despite political demands that such data should be collected and made available. Missing data may include data that are entirely absent but also data that are sparse, neglected, poorly collected and maintained, difficult to access, infrequently updated, contested, and/or underreported.
Power – The current configuration of structural privilege and structural oppression, in which some groups experience unearned advantages—because various systems have been designed by people like them and work for people like them — and other groups experience systematic and violent disadvantages — because those same systems were not designed by them or with people like them in mind. Specific manifestations of privilege and oppression include but are not limited to: cisheteropatriarchy, settler colonialism, white supremacy, racial capitalism and ableism. Hegemonic data science reinforces power and the unequal status quo it produces. Counterdata science seeks to challenge it.
Official data – Data that are produced by the state, international governing bodies, and/or other mainstream institutions such as large corporations or professional associations.
Triangulation – Using multiple sources of information about the same event or phenomenon to cross reference and verify details. This is often necessary when official data are suspect or sparse or when there is not a single authoritative source of information in the ecosystem.
What is your data epistemology?
Practitioners of counterdata science mobilize alternate epistemologies of data that challenge the extractive and violent regimes of hegemonic data science. In the last decade, the number of alternate data epistemologies has multiplied. Each one offers principles and frameworks, supportive communities and model projects, and more. The list below showcases those approaches which I am most familiar with and have been either following or participating in. If you are interested in any of these areas, then these groups are a good place to start. Or if your passion leads you elsewhere, these groups may provide inspiration and examples for your own data-driven work.
The Design Justice Network
A community of practitioners with an important set of principles focused more specifically on using design to support social justice. See https://designjustice.org/ and the book Design Justice by Sasha Costanza-Chock4.
Data 4 Black Lives
A movement of activists, organizers and mathematicians based mainly in the US which aims to use data "to create concrete and measurable change in the lives of Black people". See in particular https://d4bl.org/ and their 2021 report on Data Capitalism and Algorithmic Racism5.
Data feminism & feminist approaches
Publications, programs and emerging networks that mobilize feminist theory and activism to work with data and AI. See the:
If you are looking to put data in service of Indigenous nations and people, the global Indigenous Data Sovereignty movement has produced excellent scholarship, practical guidelines and policy. See https://indigenousdatalab.org/networks/ and the scholarship of Desi Lonebear-Rodriguez, Maggie Walters, Tahu Kakutai, and Stephanie Carroll among many others6.
Recent publications aim to show how data may be used (or refused) to center the lives and well-being of queer, trans, non-binary and LGB+ people. See the essay by Os Keyes on radical data science, the book Queer Data by Kevin Guyan and the volume of essays Queer Data edited by Patrick Keilty from University of Washington Press7.
A global community of scholars and activists working to decolonize data. See https://www.tierracomun.net/, the book The Costs of Connection: How Data is Colonizing Human Life and Appropriating it for Capitalism and the scholarship of Paola Ricaurte8 .
Principles of Data Feminism
Data feminism is one such alternate epistemology of data, and the one that comprises the conceptual backbone of this book. In Data Feminism, which Lauren F. Klein and I published in 2020, we outlined what a feminist approach to data science might look like – an alternate data epistemology to challenge the standard operating procedures of hegemonic data science. We draw from intersectional feminist theory, activism and writing to outline seven principles for working with data in a feminist way. I offer these principles here in the hopes that they will be a useful part of your toolkit, just as they have guided my own research and writing throughout this book. But I offer them with the acknowledgement that these are far from the only principles that you could use in your own data-driven work. The frameworks emerging from other alternate data epistemologies might be more relevant and useful.
Examine power. Data feminism begins by analyzing how power operates in the world.
Challenge power. Data feminism commits to challenging unequal power structures and working toward justice.
Elevate emotion and embodiment. Data feminism teaches us to value multiple forms of knowledge, including the knowledge that comes from people as living, feeling bodies in the world.
Rethink binaries and hierarchies. Data feminism requires us to challenge the gender binary, along with other systems of counting and classification that perpetuate oppression.
Embrace pluralism. Data feminism insists that the most complete knowledge comes from synthesizing multiple perspectives, with priority given to local, Indigenous, and experiential ways of knowing.
Consider context. Data feminism asserts that data are not neutral or objective. They are the products of unequal social relations, and this context is essential for conducting accurate, ethical analysis.
Make labor visible. The work of data science, like all work in the world, is the work of many hands. Data feminism makes this labor visible so that it can be recognized and valued.
Counterdata Science Examples
What does a counterdata science project look like? To be honest, I’m not sure it is possible – or desirable – to give an absolute definition of what such a project consists of. Indeed, part of the value of counterdata work is being aware of the traps that come with rigid definitions, and purposely exploring the benefits that come when we avoid those traps. That said, across my research into data activism practices and my participation in various communities of practice, I have noticed a few basic characteristics that many counterdata science projects share:
It doesn't matter how big (or small) the data set is – it can still be counterdata science.
It doesn't matter how sophisticated (or simple) the analysis is – it can still be counterdata science.
It doesn't matter whether the people doing the work are credentialed, i.e., with advanced degrees from fancy institutions or not – it can still be counterdata science.
What does matter is mobilizing alternative data epistemologies and practices in the service of transformative social change– that is, challenging power.
What does matter is ethical, long-term, relations of care with the communities most impacted.
What does matter is pluralistic and culturally appropriate conceptions of rigor and truth.
All of the grassroots feminicide monitoring projects I have discussed in this book are examples of counterdata science. Examples from other domains abound as well. Below are projects across sectors ranging from activism to journalism to urban planning.
Activism and Social Movements
Anti-eviction Mapping Project https://antievictionmap.com/ This activist-academic project defines evictions more broadly than the legal definitions, and uses their discordant data to advocate for a more structural framing of the root causes of eviction in specific areas. They acquire official eviction data from court records and other eviction data from surveys and collaborations with housing clinics. They have produced maps of evictions, landlord monitoring tools, oral histories and a book9.
Run by advocacy organization Campaign Zero, this project has tracked and mapped fatal police violence – and its systemic racial injustice – in the US since 2013. Similar to the anti-feminicide activists, the project relies on media reports as a primary source and triangulates those with official data and other counterdata sources. The database is open and publicly available.
Data on maternal mortality in the US have been characterized as "an unreliable mess" by Scientific American10. In 2016, ProPublica set out to identify every single mother or parent who died from pregnancy-related causes in the US (estimated to be between 700 to 900 people). They used social media, crowdfunding sites where funds had been set up for families left behind, public records and obituaries.
The result of a year-long investigation by the Markup, this data journalism story demonstrates systemic racial bias in home mortgage loan approvals in the US. The journalists used publicly available official data triangulated with academic research studies, but there are still key missing data hindering a comprehensive analysis – notably credit scores – which the mortgage industry has successfully lobbied to keep secret.
High Country News produced an original report and unique database documenting how land grant universities across the US were funded with expropriated Indigenous land via the 1862 Morrill Act. Data sources included land patent records, congressional documents, historical bulletins, historical maps, and more. Many data needed manual entry, and the database took months to assemble. It is publicly available for download and research.
Economist Caitlin Myers created a database that identifies the names and addresses of all facilities that publicly advertised the provision of abortion services between 2009 to 2021 in the US. She sourced data from official data sources such as state licensing offices and Planned Parenthood lists triangulated with counterdata sources such as facilities listed by Operation Rescue, an anti-abortion group. She uses the database for research on travel burdens associated with declining abortion access in the US and makes it available to others only on a restricted basis after signing a written contract.
Initiated in 2012 by a team of researchers at the Universitat Autónoma de Barcelona, in Spain, the EJ Atlas undertakes systematic collection of global ecological conflicts in partnership with activists, civil society organizations, and social movements. They source conflicts from media reports, crowdsourcing and local partnerships with impacted communities. The project has developed its own typology of ecological conflict and publishes its data openly.
A study by the arts-based nonprofit Monument Lab found that the monument landscape in the US is overwhelmingly white and male, and elevates themes of war and conquest. There is no authority in the US that keeps records on monuments, so to undertake the national audit, they assembled records from dozens of federal, state, local, tribal, and institutional sources, many of which have different criteria and definitions of "monument". They held participatory analysis sessions to develop their analytical categories and themes. The data can be explored in their interface on the project's website.
A civic media project by Ecofeminita that documents and visualizes where political candidates in Argentina stand on gender and LGBTQ+ issues, including reproductive rights, femicide, care work and trans rights. The first version was released in 2017, with subsequent versions in 2019 and 2021. Data on politicians' views were collected through media reports, candidates' public statements and policy documents and through surveys administered by the organization.
An advocacy campaign, open dataset and series of reports undertaken by Global Witness to record the unjust deaths of people – largely Indigenous people – killed while defending their land and environments.
The New Orleans City Council Street Renaming Commission audited all of its streets, parks and places for ties to slavery, exclusion, and segregation; eventually they recommended the renaming of 37 streets. The commission also came up with recommendations for how to rename places using a sound, democratic and publicly engaged process.
The Detroit Geographic Expedition and Institute (DGEI) was a collaboration between Black young adults in Detroit and white academic geographers that lasted from 1968–1971. The group worked together to produce data about aspects of the urban environment related to children and education, and produced numerous maps from that work, including the widely circulated Where Commuters Run Over Black Children on the Pointes-Downtown Track. The DGEI collected many other types of counterdata and published them in reports with analysis and recommendations11.
Stages of a Counterdata Science Project
Throughout this book I have described the different workflow stages of a counterdata science project: Resolving, Researching, Recording and Refusing and using data (see figure 2.4 and Chapters 3-6). These stages are derived from our team's interviews with grassroots data activists working to challenge feminicide. These stages form a four-stage process model and I offer that model here in the hopes that it may be useful to other practitioners working on counterdata science projects, both about gender-related violence and beyond.
Different ethical concerns and questions arise at each stage of work in a counterdata science project. For example, during the resolving stage of a project, counterdata practitioners are developing their analysis of the problem and their theory of change for how and why counting and data analysis might be useful. Here it is important to think about your own positionality in relation to the phenomenon, who the beneficiaries of a counterdata science project might be, and how to work collectively, in networks. In contrast, during the research stage of a project, it is important to reflect on the systemic biases in the information ecosystem, creative ways to source information, and how your project will handle missing data about minorities and subgroups.
Because of these differences, below I have provided activities and reflective questions for each stage of work in a counterdata science project, from getting started in the resolving stage to circulating and communicating data in the refusing stage. While there are many ways these could be used as part of a counterdata science process, here are three possible ways the toolkit could be used:
Strategic project planning and visioning. In this model, a counterdata science project team is undertaking planning sessions to map out the work ahead of them, establish a shared vision for the project, who it is serving, and how to sustain it through the different stages of work. In this model, the project team would take the time to go through the questions and activities in this Counterdata Science Toolkit, as individuals and as a group, align around their answers at each stage, and incorporate them into their plan and their vision.
Equity pauses and recalibrations. I learned about the idea of "equity pauses" from Jenn Roberts who runs VersedEd and the Colored Girls Liberation Lab12. This is the idea of regularly stepping back from the intense day-to-day work, say, of researching and logging counterdata, and pausing to evaluate your process and whether you are meeting your equity goals. In this model, counterdata science teams would take a short period of time to engage with the questions at one stage of work, discuss their answers, and surface shifts and recalibrations to make in data practices to better meet their goals. These equity pauses could happen at regular, scheduled intervals, for example, at one meeting a month.
Ethics crisis moments. There may be moments in a counterdata project that provoke an equity pause that the team did not foresee. A community may come forward and express that they have been harmed. An individual's information may have been made public in a re-traumatizing way. You or your team may have included a story or a case or some information without permission. These are moments where a more profound recalibration – of data practices and of relationships – becomes necessary. This toolkit could aid in that recalibration by providing a structured set of ethical questions and activities for the counterdata team to use to draw out their analysis of what happened, how to redress it in the short-term, and how to prevent such harm in the longer term.
As you will see in the questions and activities below, each question or activity is mapped to the data feminism principle that it aligns with. This is my attempt to demonstrate how one's data epistemology can translate into concrete matters of discussion and action for people working with data. Following the publication of Data Feminism, many people have asked Lauren and I for practical guidance on how to use the data feminism principles – to move them from general guidelines into something applicable in specific contexts. This mapping is an attempt to do that, as well as a way of inviting scholars and activists to do this kind of mapping work for other data epistemologies.
Resolving is the stage of a counterdata science project where an individual or group seeks to address a problem of structural inequality and determines how and why counting and registering data will be an effective method to do so.
Inventory existing efforts – by activists, nonprofits, journalists and academics –to count the phenomenon you are interested in. How can you join rather than initiate your own effort?
Inventory existing laws and existing data sources. How are legal protections and available data falling short?
Whose job is it to count? Whose job are you doing? How will you remind them that it is their job?
Are you from the community you are counting? Are you working within institutional structures that incentivize knowledge extraction (academia, journalism, nonprofit) or profit (industry)? Are you the right person or group to be doing this work? Try to have the courage to not do the project.
Which ideas - from theorists, activists and/or communities – will you draw from for your structural analysis of power? How does your counting work help to name and frame the phenomenon as a structural problem?
Who are you counting for?
What is your theory of change? How and why do you think measuring and monitoring will challenge power?
What are ways that counting may harm the people and communities you want to serve (for example, by making them visible to institutions that want to target them)?
Geographic scale: Are you monitoring at the scale of the neighborhood, city, province, nation-state, continent, globe? How does that match who you want to serve or influence?
Temporal scale: Are you starting now and moving forward? If you are looking back in time, how far back? How does historical monitoring affect your sources of information? How does that match who you want to serve or influence?
Is this a one-time study or an on-going observatory? How does that match your available resources (people and money)? How does that match who you want to serve or influence?
How can your project center the lived experience of those who have been impacted by the issue? Without exploitation, extraction or tokenism?
What is specific to the geography or community that you are counting? What differences do you need to highlight? Do you need to develop new names, frames, concepts and/or categories for that context?
How can you avoid hoarding, whether data or credit? How can you count in community – leveraging collectives and networks of solidarity? How can you build partnerships and work in networks instead of trying to do everything on your own?
Elevate emotion and embodiment
What kind of emotional labor is involved in this work? How will you care for yourself and your team as you measure injustice?
Researching is the stage of a counterdata science project where an individual or group seeks and finds data observations and related information to add to their database. This can include sourcing existing datasets, discovery and detection of new observations, triangulation of information across sources, as well as on-going research to add information to existing observations.
Discuss the following questions with your team before beginning research:
What harms does collecting counterdata potentially incur for the groups that you want to influence or serve?
Whose trauma does it make visible and what is your (individual/team's/institution's) relationship to that trauma?
Could bad actors use your data to target minoritized groups?
Do the counterdata perpetuate a deficit narrative about minoritized groups – painting them as in need of saving by dominant groups?
Seriously consider non-intervention before you start working. As mentioned earlier, have the integrity to not do the project if you are not the right person or people to do the project or if you may incur further harm to communities.
Inventory existing official and unofficial data sources and their limitations. Inventory what does not exist. What information is missing – including absent, underreported, inaccessible, incomplete, unreliable, and untimely data – and why?
Map mitigation strategies to your list of existing and non-existing sources of data. What are creative ways that you can navigate, mitigate and triangulate missing data? Don't forget to consider mass media, hyperlocal media, social media, private chat groups, relationships, partnerships, friendships, and crowdsourcing.
What groups, especially those at the intersection of multiple forms of domination, will still be missing, underreported, erased or neglected by your methods of counterdata research? How can you address those limitations or, at the very least, acknowledge them?
How can you cultivate human networks of counterdata research predicated on ethical, authentic, non-extractive, and enduring relations? These might be relations with individuals, social movements, coalitions, journalists, nonprofits, service organizations and more.
Elevate emotion and embodiment
How emotionally challenging is the research? How will you handle self-care and team-care for secondary trauma? What does a trauma-informed approach to the production of this data look like?
Make labor visible
How can you make the labor of researching counterdata visible and for whom? Are there strategic reasons for hiding the labor of your counterdata research? Are there ways to acknowledge labor and care internally even when it may be strategic to conceal them externally?
Recording is the stage of a counterdata science project in which involves extracting unstructured data from various sources into structured datasets (in text documents, spreadsheets and/or database management systems); classifying cases according to diverse typologies; and managing data – including ethics, access and governance of the database.
What does consent look like for the type of data you are collecting? How will you obtain consent and also support individuals and groups who decide to withdraw their consent?
What legal, professional and/or international standards exist for the phenomena that you are recording? Do they adequately capture and classify the scope of the phenomena?
How can you count and classify in order to exceed and/or challenge those standards? How might you demand that the phenomenon be conceptualized and measured differently?
What important variables and categories are missing from existing data? How can you incorporate those into your recording work?
How can your counterdata project operate as a megaphone for amplifying the voices, power, knowledge and agency of the people closest to the harms that you are trying to challenge?
How will you engage multiple and diverse stakeholders in the development of your data variables and categories? How will you participate in building community – the essential, on-going social and technical infrastructure that can sustain this work?
Who else is recording counterdata about the issue? How can you scale your impact by harmonizing with them – i.e. sharing definitions, categories, dialogue, recording tips, and even potentially pooling data for greater impact?
Rethink binaries and hierarchies
Whose experiences are sidelined, erased, marginalized by your schema and categories? Whose experiences will be sidelined because there will be quantitatively fewer of them in the dataset and/or because of known biases in the data sources? How do you bring these experiences back in?
How can you collect identity categories such as race, gender, ethnicity without naturalizing and essentializing them? Which categories might you avoid collecting because to collect them would be to do violence (e.g. trying to record someone's race from a photo of them)?
Elevate emotion and embodiment
How will you care for and respect your data? How will you develop your team's intimate knowledge of and relationship with the data?
How do your columns and categories communicate certain narratives about the issue and the people involved? (For example, is a woman always named as a "victim," defining her life and her agency by a single event?) How can you push back on that essentializing tendency?
How is your database a memorial to structural trauma - a cultural countermemory? Whose lives and whose pain is represented therein? How are you accountable to them and how are you in relationship with them?
If your database is a memorial, how does this shift your thinking about ethics and access to the database?
Refusing and using data is the stage of a counterdata science project where individuals and groups circulate data, in order to push specific actors towards thinking, feeling or acting differently. The goals of these data actions and circulations may include: to repair, to remember, to reframe, to reform, and/or to revolt.
Free write about refusal and the issue area that you are working on. What are you refusing? Who is refusing? What is the affirmative, generative vision forged from your refusal?
(For example, for anti-feminicide activists the affirmative vision is a world which has eliminated gender-related violence and its causal forces of oppression: patriarchy, settler colonialism, white supremacy, racial capitalism, cissexism, homophobia, and more.)
Counterdata action and circulation may refuse the status quo in a variety of ways. Here are some questions to start thinking about the ways your project may use data for refusal:
Repair: How can data support direct services, support and healing to the people most impacted by the issue?
Remember: How can data support the holding of collective space around a structural problem? How can data reshape collective memory and communal interpretation of an issue?
Reframe: How can data's authority be leveraged to amplify grassroots voices and analysis in media and culture? To use narrative change to do mass public consciousness-raising?
Reform: How can data be leveraged to design new or reform existing institutions, laws, policies and official practices? How can data's authority be leveraged to get a seat at the table for impacted communities?
Revolt: How can data be used in support of massive mobilizations in public spaces or online? How can data become spectacular and evocative, physically or digitally, to claim space, to claim time, to demand public attention, and to enact dissent?
Make a list of the different groups or audiences that you want to move to action and brainstorm multiple forms of data communication tailored for each audience. (For example, if you want to move policy makers to action, one form might be a report with data visualizations. Another form might be oral testimony from impacted communities. Another form might be a visual slideshow with photos, quotes and statistics. Another form might be a protest outside their offices.) Each form is an opportunity to involve different groups in the communication and the circulation of data.
Elevate emotion and embodiment
When is it politically advantageous to deploy Donna Haraway's "god trick" – to communicate data neutrally and minimally, as if from an omniscient observer? When is it more appropriate to center emotion and embodiment in data communication?
How can you recontextualize your data points and recuperate them from their abstraction into rows and columns? How can acts of data communication and circulation connect each data point back into the fullness of the life-worlds from which it emerged?
This whole book is an example in pedagogy through book-writing, but especially the way this chapter was written! So much to bring into counterdata science practice.
A footnote with a reference for “Donna Haraway's "god trick”” would be useful.
(I can’t annotate this text directly as it’s in a table)
The statements listed below (e.g., “It doesn't matter how big (or small) the data set is“) aren’t actually shared characteristics.
You could re-phrase to something like:
”I have made a few observations about what characteristics counterdata science projects do (and do not!) share:”
I think that you and Lauren wrote it, but MIT Press published it.
Would the meaning of the sentence change if this word was deleted?
In the next sentence, “alternate” indicates a contrasts with “hegemonic data science“, but I’m not sure it is needed in this sentence.
Personally I strongly dislike the expansive use of "triangulation" as a metaphor to refer to any process of combining data.
It has a very specific meaning of combining *angles* to obtain a position (distinguished from other similar terms such "trilateration" when measurements of distances rather than angles are combined).
There are more appropriate alternative terms such as "merging" datasets, "data integration", or "data curation".
Some of the uses of “triangulates” below could be replaced by “combines“.
In the definitions, consider typographically distinguishing terms that themselves have definitions listed (e.g., the definition of “counterdata” refers to “official data and “missing data“, which are defied below, and the definition of “Counterdata science“ refers to “hegemonic data science“) - e.g., by writing these terms in bold too.