Heavy Processing for Digital Materials (More Than A Feeling)

Jas Rault & T.L. Cowan

Part III – Risking IT: Breaking up with Compulsory Dispossessivity 

(See Part I Lesbian Processing and Part II Central Processing Units)

Heavy processing is a set of structural knowledge-making practices that come from and are informed by many intellectual and movement-building traditions, including, but not exclusive to, trans- feminist and queer (and lesbian) political and scholarly activism. We situate heavy processing as an information technology in order to understand it amongst the “fundamental information processes such as the acquisition of information and its storage, manipulation, retrieval, dissemination*, or usage” [*we would use the term circulation here] (Schuster, Alfons Josef Understanding Information: From the Big Bang to Big Data 2017, vii). Within Information Studies, information itself tends to be defined basically as “processed data that improves our knowledge, enabling us to take decisions and initiate actions” (Rajaraman, Introduction to Information Technology, 2018, 1). Quite simply, we want to draw attention to the multiple, old and new, genealogies which show that better information, from which we create better knowledges, better stories, make better decisions and take better actions, is not just processed but heavy-processed. As Patrick Keilty and Rebecca Dean put it, “the field [information studies] must engage with cultural and humanistic modes of inquiry if we are to understand the connection between information, technology, and culture” (Feminist and Queer Information Studies Reader 2013, 5). We see heavy processing as one of these cultural and humanistic modes. As we write in the Manifest-No, “data [and information] is both an interpretation and in need of interpretation” (Feminist Data Manifesto-No 2019, thank you Joan Scott 1991).

Following Michael Buckland’s definitional work on “information-as-thing, information-as-knowledge and information-as-process,” Tanya E. Clements notes that the study of information-as-process is the study of “the systems of power and influence that shape information systems and therefore knowledge production, identity construction, and intersubjectivity” (Clement, Tanya E. “Where Is Methodology in Digital Humanities?” in Debates in the Digital Humanities 2016). The heavy part of heavy-processing works to contend with and divest from the prevailing systems of power and influence that shape the information by which we produce knowledge. Valuing heavy processing over productivity — generally the most valued, and thus most rewarded, outcome within institutional research economies — is risky. Heavy processing might mean divesting from systems of power and influence (by which we complete our PhD dissertations in a ‘timely manner,’ by which we are published, hired and promoted) to prioritize social systems of accountability, uncertainty, self-critique, deferred authority, non-extractive knowledge making and sharing, resource redistribution and reparation, fair labour and reciprocality (Bailey 2015; Luka & Millette 2018; Caswell & Cifor 2016; Kim & Stommel 2018; Zimmer & Kinder-Kurlanda 2017; Moravec 2018; Losh & Wernimont 2018; Cole, Mobley, Wernimont, Bailey, Cowan and Paredes 2018).

In the fields of digital scholarship, critical data and information studies, Indigenous research and community leadership has continued to demonstrate that the most rigorous methods for generating better, more accountable and more true knowledges are process- and protocol-heavy. For example, the Global Indigenous Data Alliance (GIDA) draws attention to some of the under-processed assumptions fueling scholarly (and other) enthusiasm for open access and open data. GIDA argues that “the current movement toward open data and open science does not fully engage with Indigenous Peoples’ rights and interests,” and so have proposed the principles of CARE (Collective benefit, Authority to control, Responsibility, Ethics) to supplement the existing FAIR principles (Findable, Accessible, Interoperable, Reusable) (Research Data Alliance International Indigenous Data Sovereignty Interest Group, “CARE Principles for Indigenous Data Governance.” The Global Indigenous Data Alliance, 2019). The FAIR principles emerge from growing international collaborations between researchers in education, libraries, museums and archives, government, science and medicine to build digital infrastructure and standards for storing, sharing and using data (which include organizations like the Research Data Alliance and GoFAIR). As GIDA puts it, “The emphasis on greater data sharing alone creates a tension for Indigenous Peoples who are also asserting greater control over the application and use of Indigenous data and Indigenous Knowledge for collective benefit” (2019). Whereas FAIR principles of Findability, Accessibility, Interoperability, Reusability are concerned with technical applications that prioritize end-users, CARE principles of Collective benefit, Authority to control, Responsibility and Ethics prioritize processual values that are concerned with communities of origin.

Centuries of settler research culture have devalued, dismissed, undermined and eradicated process and protocol in the pursuit of individual reputation, field formation, and intellectual capital. Thinking with a process principle, it becomes evident that what Aileen Moreton-Robinson (2015) calls “the white possessive,” is characterized by collective and compulsory willful, habitual, and institutionalized disregard and disdain for traditional protocols and community-processes. Combined, this practiced and trained disregard and disdain are the imperial-colonial information technologies that extract data and generate genocidal information logics. Data collected in this way may very well be ‘findable, accessible, interoperable, and reusable,’ but not necessarily fair.

We’ve been thinking of the pull towards scaled-up open-access research economies and the settler colonial assumption that everything should be accessible (to white settlers). This is a prevailing affective infrastructure that we have come to think of as compulsory dispossessive normativity, or compulsory dispossessivity: the emotional and more-than-rational sense of entitlement that fuels, compels and rewards extractive intimate intellectual occupations. The current compulsion to digital reproduction and open-access offers researchers the opportunity to denaturalize our participation in some of these systems of exploitation and to reorient our work towards anti-colonial and anti-racist research habits, protocols and relationships. These are hard habits to break.

Jennifer Wemigwans’ book, A Digital Bundle: Protecting and Promoting Indigenous Knowledge Online, is deeply instructive for those of us TFQ settler scholars working to divest from colonial information technologies even as we engage with the affordances of online architectures and digital networks. Wemigwans connects Indigenous traditional knowledges of renewal, preservation and intergenerational transfer to “the field of new technologies and Internet studies” (2) — reflecting on the creation of her website, FourDirectionsTeachings.com, as an effort to online Traditional Knowledge in ways that follow Indigenous protocols. She introduces the concept of the “digital bundle” to distinguish her project from colonial forms of using and putting Indigenous materials online. As she explains, “we have to be mindful be of the care and passing on of bundles (whether physical bundles, special bundles of knowledge, or the gifts that we receive at birth), that they are sacred things, and that there is—or at least could be—a ceremony to go along with that process” (35). That is, a bundle is not simply an object, or collection of objects, but a gift with deep roots in place and context given in a way and by a person (or persons or more-than-person) authorized by that context. Therefore,

FourDirectionsTeachings.com can be considered as a digital bundle because it is a collection of teachings by respected Elders and Traditional Teachers who have shared Indigenous Knowledge that is highly regarded and valued by diverse Indigenous communities. These communities see FourDirectionsTeachings.com as a representation of Indigenous Knowledge ultimately derived from sacred sources—knowledge that must be respected, cared for, and passed down for future generations and hence has the attributes of a community bundle. (36)

Understanding the Traditional Knowledges gathered online at FourDirectionsTeachings.com as ‘digital bundles’ involves a complete divestment from settler colonial academic digital protocols which ask us to treat ‘found’ materials as resources to be extracted from their origin and context (think scanning and tagging, data mining, text mining, affect mining) and circulated to as wide an audience as possible.

Working from an epistemic position of Indigenous resurgence Wemigwans asks,

Beyond safeguarding cultural heritage, how do we protect the flow of communication and access to Indigenous Knowledge for the next seven generations? Knowing that net neutrality is not a given and that access to the Internet and ICTs is not a government guarantee, how do Indigenous Peoples safeguard freedom of expression and access to Indigenous Knowledge online for future generations? (2)

Wemigwans situates the responsibility for and complexity of both safeguarding and communicating Indigenous Knowledge in a much bigger understanding of temporality than most new technology and new media studies. Citing very different concerns than the usual individualized privacy/security preoccupation of corporate platforms, her concern is about the importance of ensuring future generations’ group access by safeguarding Indigenous knowledge protocols. As Wemigwans makes clear, when working with Indigenous knowledges there is an important distinction between sacred and personal knowledges, and they involve very different kinds of protocols:

Sacred teachings consist of Traditional Knowledge passed on through ceremonial protocols. Only Elders and Traditional Teachers who have been gifted the Indigenous Knowledge and teachings in this way can share those teachings publicly and transfer them. This type of Indigenous Knowledge is often considered as belonging to the community and held in trust by Knowledge Keepers and Elders expected to abide by the cultural protocols entrusted to that knowledge.

Personal knowledge is acquired through individual educational pursuits, empirical processes, or the gifts that one is born with or has received through revealed knowledge, which includes spiritual knowledge gained through dreams, visions, intuitions, and meditations. Personal knowledge is not bounded by the cultural protocols of the community in the way that Traditional Knowledge is. (3)

TFQ heavy processing is not the same as Indigenous knowledge-transfer protocols. While TFQ heavy process is concerned with CARE principles, we must insist on remembering that what we are talking about in settler TFQ communities does not compare to the sacred ways of working that Wemigwans and many others detail are the “ceremonial protocols” led by Elders, Traditional Teachers, and Knowledge Keepers for knowledge transfer and remediation.

The connection we make here between TFQ community methods and Indigenous protocols is not to claim sameness, but, rather, to identify a possible set of affinities. There are many different kinds of what might be broadly (and, usefully, we hope) characterized as heavy processing, or process-heavy research practices. It is our hope that identifying this affinity for ways of heavy processing might allow us to work across fields and research communities of origin to together shift research cultures, economies, temporalities and standards of rigour.

As a settler scholar who has practiced protocol-rich methods for designing digital content management projects with collaborators “from six tribes — Colville, Coeur d’Alene, Spokane, Umatilla, Yakama, and Warm Spring” (2018, 408), Kim Christen proposes a way of working which she calls “ETHICS (Engage, Talk, Help, Invest, Create, Support)… [as] a framework for respectful digital archiving projects that create not just records, but relationships” (2018, 411). While each element of this six-part approach to building ethical digital archives with Indigenous peoples and their materials — and we would add ethical forms of digitizing, researching, finding, using and/or onlining minoritized peoples cultural materials — involves the process-heavy commitment to relationships and accountability, the second principle, “talk,” resonates especially for our heavy-process affinity tracing: “Start by talking face-to-face with all interested parties. And then talk some more, and talk a little bit more. Often specific needs will unfold over the course of several in-person gatherings” (2018, 410). The commitment to talking and more talking, over time, through several different meetings in different settings is a significant step away from the settler colonial governmental discourse on the ‘duty to consult’ — where consultation, or what Jas calls “consultative dispossession” (2020), has historically and consistently worked against the interests of Indigenous peoples. Christen notes that researchers doing this work for museums, archives and universities need to meet outside those institutions: “go to the communities you want to engage, attend their public meetings, and do not have all your interactions in a university setting. Power rests in places” (410). As Christen clarifies, prioritizing relationships means turning away from

the general “get it, curate it, share it” model and expand[ing] it to include cultural, ethical, and historical checks at each step, then we get a workflow that encourages collaboration, relies on historical specificity, and has ethical considerations embedded at every step. Finding or discovery should not be guided by a search paradigm that disregards the colonial histories of collection or upholds notions of access that privilege the public domain. (2018, 407)

Committing to process-heavy ethical digital research — which builds-in workflows (and concomitant timelines) for cultural, historical ‘checks,’ collaboration and talking at every step, can be understood as part of the research design process for defamiliarizing and not reproducing the colonial histories of collection and notions of public access.

For those of us working in digital research environments, such relationship-forward, context-specific, heavy-processing critical research needs to be applied as much to our materials as to the digital tools we use and the knowledge systems that these tools reproduce. Jennifer Guiliano and Carolyn Heitman draw attention to the ways that humanities data are processed through settler colonial information technologies: “From ink and quill maps representing the New World to the carefully stratified layers of an archeological site, data in the humanities are always subject to the systems of knowledge that were used to capture, represent, and disseminate [or circulate] them” (2019, 1). Such systems of knowledge are baked into most of our digital research tools (what some will call digital methods), including the technologies that have enabled so much of the field of the digital humanities:

The advent of digital data aggregation, linked open data and computer vision (machine reading) techniques also raise additional concerns with the regard to the reuse and circulation of Native American and Indigenous data. Machine learning processes used to classify and categorize digital images rely on the segmentation of patterns. This can include the physical segmentation of bodies of Native people (e.g., faces, heads)—a form of violence that mirrors colonial practices where Native […people] are treated as less than human through segmented image representation (e.g. scalps, severed limbs, etc.). What’s more, these computational processes further decontextualize and reappropriate culturally sensitive images of Native people, places, and practices. (2019, 8)

If the information technologies upon which we rely — from the systems of knowledge our academic disciplines inherit and enforce, to the digital tools we employ — reproduce settler colonial, anti-Black and anti-TFQ logics, violences and relations of power, we need to stop, step back, invest in some heavy processing towards the creation of new information technologies.

The rigour of heavy processing is a time-sensitive, time-intensive and sense-intensive method which asks researchers, perhaps especially those working in digital (humanities) projects, to turn against many of the standards of rigour (and speed) guiding contemporary scholarship. As the Assembly of First Nations puts it, in “Ethics in First Nations Research” (Assembly of First Nations 2009),

In many cases, it takes more time and money to conduct research ethically. For researchers attempting to conduct themselves in an ethical fashion, it will be necessary to withstand pressure to complete projects according to rigid funding timelines and external expectations for publication. (31)

The time and money clash between productivity and ethics is a familiar one. For example, Jesse Daniels and Polly Thistlewaite reflect on the limitations imposed by the academic funding clock in the community feedback they received about their “participatory, open, online course” (POOC) titled “Reassessing Inequality and Re-Imagining the 21st-Century: East Harlem Focus” (also known as #InQ13) at the City University of New York’s (CUNY) Graduate Center and Hunter College (Daniels and Thistlewaite 2016). While the project attempted to be engaged with and embedded in the East Harlem community in New York City, and certainly was created with the best intentions, the community-engagement suffered from temporal and financial limitations, echoing in a way, The Assembly of First Nations caveat above. Daniels and Thistlewaite write:

The #InQ13 collective also included 18 community partners in East Harlem, and here we were less successful. The community partners we spoke with had several complaints about our project, all of them entirely valid. They said that we had come to them too late in the process, which we had. Our project, only funded for one calendar year, sometimes operated at a breakneck pace that was not conducive to the long, cautious process of relationship building necessary for community engagement. Several distrusted the university as a whole and, more specifically, objected to a course about East Harlem that was taught by CUNY faculty rather than by residents of the neighborhood. This highlighted the inequality between the university and the community we wanted to engage. If we had had the luxury of more time, we could have found more innovative ways to staff the course. (Daniels and Thistlewaite 54)

Their “participatory” and “open” project — which sought to work against academic gentrification and alienation — was only funded for one year (an impossible amount of time to make anything truly participatory or open); this is a common paradox. Daniels and Thistlewaite’s reflections highlight the ways that the academic cycle works against the heavy processing required for networked, online and offline community-partnered research and teaching, since our projects are generally contingent upon grants and the restrictions they bring with them. The criticisms the #InQ13 organizers received had to do with belated contact with community members, and that the high speed required to build and complete a project in this time, led to it being neither as participatory, nor as open as the organizers would have hoped. We appreciate and learn from Daniels and Thistlewaite’s honest reporting-back, as a form of feminist, queer processing and accountability.

We write this having just applied for multiple grants that have a one-year (or less) timeline, and having just requested extensions for several of the one-year grants we received (and for which we are grateful) last year, at the University of Toronto. We have come to think of heavy processing as a critical digital method that, by attempting to push back against the invented and incentivized academic clock, is necessary for the most rigorous digital scholarship. As we learn from our own and our colleagues’ experiments, turning back time isn’t an option once a project is complete. It becomes our collective responsibility, then, to insist on enough time to put into place the process-heavy methods we need in order to do our work well, or in a good way.

“Heavy” by T.L. Cowan. Visual description: Photograph of a dishwasher’s “heavy” cycle button, which is illuminated and florescent green.

Heavy Processing: Ritual and Citation  

We frame heavy processing as an information technology to draw attention to ways of working against the violences performed by other information technologies, other data processes. We hope to indicate the ways that process-heavy research methods and protocols might operate as a countermeasure against machine learning and other digital information technologies that reproduce epistemic and physical violence. In “Making a Killing: On Race, Ritual, and (Re) Membering in Digital Culture,” Tonia Sutherland takes up the mechanisms by which networked digital platforms reproduce, and profit from, spectacles of Black suffering and death. Thinking about the need for mourning rituals and data reclamation in the aftermath of police killings of Black Americans, Sutherland carefully follows the digital visual reproduction and circulation of the death of eighteen year old Michael Brown, killed by a Ferguson (Missouri) law enforcement officer in 2014: “the hypervisual circumstances of Brown’s death and the four hours that his body lay exposed on the street, and the ensuing documentary practices usurped community mourning rituals that would typically preserve and extend community bonds” (2017, 34). Sutherland puts the often-predatory and malicious digital documentation of dead and dying Black bodies into the context of African American and ancient rituals for grieving and home-going:

Black Americans have specific rituals around grieving, mourning, and death, and for many, death is not seen as an ending but as an important transitional ritual. Because for black Americans grief frequently occurs in the context of a substantially shorter life expectancy than for white Americans, that grief is often experienced alongside entanglements of anger, resentment, and feelings of injustice….For communities under the siege of what looks like state-sponsored violence and otherwise in crisis, controlling postmortem narratives and images of the deceased is one way to re(member) the dead. (2017, 34, 35)

Sutherland draws attention to the ways that past and present technologies — from postcards to autoplay — for the mass and uncontrolled circulation of images of violent Black death (of many, many Black deaths) are, largely, compulsive and compulsory dispossessive remediation processes that serve and sustain white supremacy. Rather than processing in ways that centre the grieving families and communities and puts control of these images and stories in their hands, the digital image complex, while fed by humans taking and uploading photographs and videos, is fueled and accelerated by AI and other information technologies that treat all images as data to be sorted and coded for search engine optimization. This process cleans human-centred grief, rage and injustice, as well as attachment, joy, love, kinship, collective and personal memory from the data. Algorithmic processes of search engines and online social media platforms reproduce images and videos for a maximalized, undifferentiated mass audience and have no capacity to consider and make decisions based on the vastly different impacts and attachments these images acquire between those family and community members in mourning, their comrades and accomplices, and curious onlookers, complicit bystanders, and outright haters.

As Sutherland explains, it is not necessarily the images themselves that dishonour the heaviness of these state-sanctioned, or police- and state-involved deaths, but the remediation technologies of automated and infinite recirculation that capitalize on and fuel white supremacy, which claims entitlement to know, own, control and humiliate Black bodies in life and death. Whiteness and racial capitalism thrive on the platform economies of these information technologies responding to the ever-repeating, expanding, rapacious and decontextualized visual consumption of Black death(s) as a national and global obsession (2017, 34). Corporate information technologies recalibrate digital visuality and human- and machine-circulation of images in heartless ways. For example, as Sutherland finds, “[i]n the years since Brown’s death, Google Images has created several classes of filters for photographs of Brown such as ‘dead,’ ‘4 hours,’ and ‘the street’” (2017, 34). These filters process images in the service of consumption, not mourning or remembering. Sutherland argues that

while communities of color have long engaged in ritual practices of (re)membering and bearing witness to violent acts as modes of resistance and mourning[,] in digital spaces these practices have been appropriated to reinforce systems of white supremacist power and racial inequality, re-inscribing structural and systemic racism. (2017, 33)

Platforms like Facebook, for example, might seem to offer space for, play host to, replicate and even to encourage online gathering that foster community mourning; however, “the spectacle of black death that replays itself without purpose or context is traumatic” (38). These platforms not only “make a killing” from Black death(s), but by stripping the images of their life-contexts, they also reproduce unsafety for Black, Indigenous, Trans- and other hate-targeted people.

The process of creating safety and context is offloaded from the platform to the users (people) most harmed. The hybrid online-offline, emotional-technological ritual work of remembering and grieving in and beyond digital culture, Sutherland explains, “becomes the urgent matter of binding the uncontrollable, an intentional act of making the trauma, and the space where the trauma is encountered, safe” (38). For Sutherland, this becomes the “emotional labour” of “bearing witness” and of seeking out these uncontrollable “digital records and creat[ing] safe spaces in which to experience them” (38), and creating new forms of ritual. Safe-making is risky work — implicating and risking one’s own wellbeing in the research process and outcomes.

Sutherland’s practice has been central to our understanding of what we call “Risking IT” in the title of this section. As Sutherland describes her own process, we see and feel this as a both a mode of human-computer interaction and a human-centred information system that involves absorbing and processing the heaviness of a moment, of a history, of collective futurity, both grounding this heaviness in the context of bounded networked connections, relationships and realities, and offering some recourse to the manufactured uncontrollability of massive and automated digital visual economies of anti-Black racism. Sutherland’s work, and her own processing of these images operates like a counter-program and counter-process to the automated replay and sorting of SEO and other algorithmic processes. Sutherland (also a co-author of the Feminist Data Manifest-No) explains the weight of having to resist automation and the circulation of images and data in perpetuity. Her counter-process “prepares corpuses of data to be laid to rest when they are not being used in service to the people about whom they were created” (Feminist Data Manifest-No).

In “Markup Bodies: Black [Life] Studies and Slavery [Death] Studies at the Digital Crossroads,” Jessica Marie Johnson writes that “black digital practice is the interface by which black freedom struggles challenge reproduction of black death and commodification, countering the presumed neutrality of the digital” (2018, 58-59). This challenge is issued by centering Black life and futurity, even when focusing on death, enslavement and dehumanization. As Johnson explains, Black digital practice

requires researchers to witness and remark on the marked and unmarked bodies, the ones that defy computation, and finds ways to hold the null values up to the light. It compels designers to collaborate with the living descendants of the enslaved, who still claim as ancestor and kin those who can only be rendered in databases as “1” or a single pièce d’Inde. (2018, 70-71)

Johnson situates Black digital (and data) work within long histories of Black freedom practices (media use and creation, activism, scholarship), which call the researcher into relation and collaboration with lives past, present and future. This work of collaboration, “challenges slavery scholars and digital humanists to feel this pain and infuse their work with a methodology and praxis that centers the descendants of the enslaved, grapples with the uncomfortable, messy, and unquantifiable, and in doing so, refuses disposability” (2018, 71). This process requires complexity, time, compassion, the feeling-knowing that even the long-passed have life and putting the needs of descendants at the top of a priority list. What we are calling heavy processing might resonate with what Johnson identifies as “refus[ing] disposability.” Heavy processing requires that researchers contend with the weight of their collaborators’ feelings, needs and desired outcomes as consequential to our work, not as information that we can choose to take or leave as it suits our academic ambitions, timeline or budget clock.

Engaging in heavy processing as the norm not the exception in digital humanities, science and technology studies, information studies, digital media studies, social media studies and data studies, would mean that, rather than looking for the ‘clean’ line through a research problem or question, researchers would understand all of this work as inherently entangled, embodied, context-rich, and consequential. Jacqueline Wernimont concludes Numbered Lives: Life and Death in Quantum Media,

with a call to rematerialize data, to make it into something that one can touch, feel, own, give, share, and spend time with… allow[ing] us to engage mediation with a different ethos… [so] that we might imagine a resistant engagement that acknowledges the violence, and confronts it to imagine alternative ways of being, becoming, and dying with our media. (2018, 163)

For several years our thinking about heavy processing has been greatly influenced by a Vibrant Lives workshop called “Hearing Eugenics” (created by Wernimont and collaborators Jessica Rajko and Eileen Standley), which we attended at the 2016 FemTechNet conference at the University of Michigan. In it, we heard — or experienced sonifications of — the heavy data on eugenic sterilization in California (1921-1953), pitching age, gender, nationality and ethnicity through coded variations of electronic sound. In the various research projects, workshops and experiments devised by Vibrant Lives, heavy numbers become processed through multiple media and embodied engagement — in sound, movement patterns and exercises, haptic designs and textile weaving — as information technologies that ‘rematerialize data.’

Heavy-processing characterizes many innovative materialist feminist information technologies, including “duoethnography,”  a “feminist methodological tool for collaboratively researching complex and everyday interactions between users, devices, and data, sites, and socio-technical systems” (2019, 190:18), designed and practiced by Marika Cifor and Patricia Garcia. Working together on health-tracking devices using journaling, shared notes and other “personal” forms, Cifor and Garcia “propose and describe four facets of the methodology: relationality, difference, dialogic process, and critical subjectivity” (2019, 190:1). Not unlike the feminist manifestos and feminist research environments like CLEAR that we discuss in Part II of this series, the “collaborative intentionality of duoethnography […] views the personal as a valuable site of knowledge production, positions knowledge formation as a dialogic process, and promotes alternative ways of knowing and meaning making” (2019, 190:1) in STS and the study of digital technoculture. Usually TMI implies an aversion to too much personal information. But Cifor and Garcia’s duoethnography insists on crossing the TMI threshold, moving into the NEI (not, or never, enough information) territory of heavy processing. Not only does heavy processing require us to deal with heaviness in accountable, creative, non-derivative and non-disposing ways, it also requires us to put our own bodies on the line and to attend to research as risky, embodied, personal, entangled, complicated and contradictory.

Ultimately, contending with the heaviness of our materials will lead us to increasingly heavy processes. Returning to the TMI/NEI threshold, we are reminded of Jen Jack Gieseking’s research on data visualization of the “lives of trans- youth through Tumblr posts” (2020). This is a project that Gieseking initiated many years ago but, as they write, their “research to date has been limited because it soon became apparent that studying patterns of text without being aware of the context of trans youths’ experiences lead to the misinterpretation of their experiences and arguments” (2020). Even though it is certainly the disciplinary norm to anonymously and ostensibly invisibly and objectively extract data created or left by social media users, and to publish whatever findings suit the researcher’s narrative or needs, Gieseking decided this was NEI. Rather than relying on industry-standard digital tools for data visualizations, patterns identification and subsequent research conclusions, which abstract these stories from “living individuals,” Gieseking is developing process-heavy methods to avoid decontexualizing trans youths’ Tumblr “conversations and stories” (Gieseking 2020). Gieseking’s research remains rigorously in flux as they develop a

future participatory action research project [that] will involve trans youth offering insights and feedback on these in-process data visualizations, with the goal of creating a systematic series of ethical guidelines (per and across data visualization platforms/approaches) in order to strengthen digital humanities research on behalf of social justice. (2020)

Learning from almost a decade of researching “trans Tumblr,” Gieseking is designing a research process that includes collaboration with trans youth who may be invested in the outcomes of research that may have relevance to their past, present and future lives. TFQ heavy processing is attentive to the ways that extraction research logics are also justified by abstraction logics, which insist on the anonymity, inanimity and objectivity of researchers and their tools in the context of internet research, platform studies, and science and technology studies. Extraction and abstraction, however, are the research processes necessary for severing lives from data.

Conclusion: Gravidtas

The scholarship with which we are most interested has taken up the ways that digital technoculture reinforces white supremacy and settler colonialism at the level of operating system, archival protocols, content management, machine reading, data aggregation and algorithmic design. This offloads the heavy processing away from new technologies and on to the same old (and young) people (TFQ BIPOC). This is work that lesbian, trans- feminist and queer, Indigenous, Black and people of colour have been creatively and collaboratively undertaking for generations. When we trace, understand and value these long genealogies and vast affinities of heavy processing as innovations in information technology (operating systems and central processing units), we open the fields of information studies, data studies, media studies, and science and technology studies (to name only the most obvious) to critical digital methods, ICTs and infrastructures that have been largely overlooked by academic research.

A few years ago, a peer review we received included the following statement:

This piece is gravid with citation. In a sense, this is to be expected, given the authors’ concern with responsible archiving practices. But at times it is hard to tell how and to what extent the piece is intervening. […] The takeaway from this should be: given that the majority of the essay is given over to examples and citations, if there is an original theoretical argument being made in this piece, I want to be clearer about what it is. (Anonymous 2017; emphasis in original).

Thank you anonymous reviewer. We have received variations of this comment for years, but this one was given with the most flourish and has, ultimately, been most helpful. First, let’s consider gravid. The most common meaning of “gravid” is “pregnant, heavy with young” (OED). Its etymology goes to the Latin “gravidus” to mean “burdened, heavy” (OED). As white scholars raised and educated in settler colonial contexts and institutions, our work is as much about breaking the cycle of white settler reproduction (refusing to have its babies) — losing our kin, as Christina Sharpe puts it (Sharpe 2016) — as it is about taking on the heaviness of the violences enacted and systematized for our protection (Taylor 2020; Shotwell 2018).

In Part I of this series we write, for heavy processing, there is no such thing as TMI (Too Much Information), only NEI (Not Enough Information). The same goes for citation. There is never enough citation, and citation is never enough (Cowan 2017; Ahmed 2013 & 2017). Additionally, as we write in Part I, to gain a reputation for being into processing, is to be negatively queerly feminized as a lesbian, a feminist, a girl, a woman, a queer, a pussy, a faggot, an activist, a therapist, or someone who is in therapy. Thus, for our citational practice to be feminized as gravid, as pregnant, heavy with young, burdened, is surprisingly generative as a way to understand our process-heavy way of working. However, rather than being heavy with young, although certainly some of the people we cite are younger than we are, we are heavy with what has come before, heavy with genealogy. We are not particularly concerned with making an ‘original theoretical argument’ here, and believe that much of the expectation for ‘originality’ in Western academia is a colonial trick of pretending that knowledge is the result of individual genius (rather than ALWAYS the result of a community of thinkers, even if, for the longest periods of history, that community of thinkers was primarily made up of the genius’s invisible wife and typist). If we make an original contribution here, it might just be the paths backwards we trace across several genealogies of process-heavy research methods, bringing the long derided history of lesbian processing within calling distance of more ancient and more sacred traditions. Again, lesbian processing has done its fair share of damage and has often been used as a method of exclusion and TERF-keeping. Like any method, it can be used for good and evil. But we hope to point to what TFQ heavy processing can bring to the ongoing futures of digital research practice: not only an orientation to the ethics and good-practices of process-heavy methods, but also to the pleasure, sociality, and trust in never-ending relationship-building that is not ashamed or afraid to be both feminized, queered and sexualised.

(See Part I Lesbian Processing and Part II Central Processing Units)

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Our ideas about heavy processing have been encouraged, informed and re-formed in the context of many important relationships and friendships, and ongoing discussions with students including Jessica Lapp, Carina (Islandia) Guzmán, Itzayana Gutiérrez, Moska Rokay, Henria Aton, Stephen Lawson, Elisha Lim, Nelanthi Hewa and Chido Muchemwa; as well as research fellows Jessica Caporusso, Sara Shroff, Naveen Minai, Cass Adair and Emily Simmonds; and many, many colleagues and friends including those here at the University of Toronto who we met in the context of the Refusal and Repair Working Group, the Consent and Its Discontents Working Group and the Monday Night Seminars at the McLuhan Centre for Culture and Technology; the Technoscience Research Unit (TRU); everyone involved in “The Labour of Being Studied/ The Labour of Refusing to Be Studied” workshop at U of T (thank you Jackman Humanities Institute);  friends and colleagues near and far in the Feminist Data Manifest-No Workshop, the Feminist Technology Network (FemTechNet) and the Centre for Solutions to Online Violence (CSOV); everyone involved in the Digital Non-Neutrality: Decolonizing and Queering DH series (WGSS) and the DH Lab at Yale University; and our Feminist Mutual Mentoring group at The New School. Special thank you to Cait McKinney for a massively generous early reading of this series.

In the coming weeks and months, we will be posting new work on heavy processing in this More than a Feeling series. Watch out for upcoming posts, including Jessica Lapp’s take on Heavy Processing in feminist archiving.