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Designing ethnographic research on the technoscience workforce according to intersectionality theory presents both opportunities and constraints. On the one hand, the pursuit of justice in technoscience requires attending to differences between scientists who have been disenfranchised from knowledge production due to racism and sexism. On the other hand, sharing the lived experiences of severely underrepresented members of technoscience heightens the risk of harm. I introduce a practice called Sheltering, inspired by the computer science technique of “black boxing” and feminist methodology of “strong objectivity.” The opacity of the shelter in which some data resides is balanced with the transparency of the researcher’s positionality. Combining reflexivity, refusal, and performative design, Sheltering contests dominant norms in science, while minimizing risks of retaliation to collaborators. It also balances communal responsibilities with research integrity. It not only requires consideration for the researcher’s relationship with collaborators, but also attention to power in the worlds they navigate and solidarity in their struggles. Sheltering, a repertoire of care tactics to protest epistemic and social injustice in US knowledge production, can help transform who gets to produce science and reimagine other ways of knowing. 

Collaborative research between scholars of science and technology studies (STS)and scholars of science, technology, engineering, and math (STEM) is a growing trend. The papers assembled in thisSpecial Section offer both embodied and empirical knowledge on how ethnographers negotiate our roles in integrative research when constrained by what our technoscientific collaborators value, what funders demand, what our home institutions expect, what we want to learn from the worlds we study, and the social transformations we envision in science and society. We grapple with how we as ethnographers can best balance caring for the communities we study, the ones we serve, and the ones we identify with. We take care that knowledge making is political. Race, gender, class, and ability status of scholars intersect with the organizational, institutional, and cultural contexts in which we practice science to shape and be shaped by entrenched power relations.Through a feminist politics of care, this collection transforms tensions in interdisciplinary collaborations into resources that enlarge our understandings of what these collaborations are like for STS ethnographers, make visible certain labors within them and, crucially, enrich our vision for what we want these collaborations to be. 

This paper offers a reflexive analysis of an interdisciplinary and cross-race collaboration to advance equity in engineering called LATTICE (Launching Academics on the Tenure-Track: an Intentional Community in Engineering). We engage two bodies of scholarship—matters of care in feminist science and technology studies (STS) and critical race theory on counterspaces—to theorize on the data infrastructure and narrative practices that we developed when applying critical methodologies to collective action in technoscience. We discuss how our care practices conflicted with traditional ethnographic practices and thus, inspired us to innovate on methods. These methods—member-checking and polyvocal memo-ing—make transgressing the boundaries of LATTICE counterspaces for public dissemination possible by invoking caring as praxis. We conclude that using these methods to discuss the contradictions and challenges in STS collaborations is an opportunity for advancing mutual intelligibility among interdisciplinary scholars and a politics of knowledge production grounded in values of care and friendship that may contribute to equity and justice in technoscience. 

The social impacts of computer technology are often glorified in public discourse, but there is growing concern about its actual effects on society. In this article, we ask: how does “consent” as an analytical framework make visible the social dynamics and power relations in the capture, extraction, and labor of data science knowledge production? We hypothesize that a form of boundary violation in data science workplaces—gender harassment—may correlate with the ways humans’ lived experiences are extracted to produce Big Data. The concept of consent offers a useful way to draw comparisons between gender relations in data science and the means by which machines are trained to learn and reason. Inspired by how Big Tech leaders describe unsupervised machine learning, and the co-optation of “revolutionary” rhetoric they use to do so, we introduce a concept we call “techniques of invisibility.” Techniques of invisibility are the ways in which an extreme imbalance between exposure and opacity, demarcated along fault lines of power, are fabricated and maintained, closing down the possibility for bidirectional transparency in the production and applications of algorithms. Further, techniques of invisibility, which we group into two categories—epistemic injustice and the Brotherhood—include acts of subjection by powerful actors in data science designed to quell resistance to exploitative relations. These techniques may be useful in making further connections between epistemic violence, sexism, and surveillance, sussing out persistent boundary violations in data science to render the social in data science visible, and open to scrutiny and debate. 

Articulating a Succinct Description uses ethnographic data to create case study interventions facilitated with people who belong to the culture with whom the ethnographer is engaged. We do so in order to disseminate research findings, address problems presented in the case, and collect additional data for further collective analysis. Further, Articulating a Succinct Description is designed as a means of intervention for underrepresented group members to be heard and gain support and promote equity engagement among majority members in efforts to create more inclusive cultures. In this paper, we validate this method using findings from its application with engineering students at a public university. This method allowed us to view engineering culture not as monolithic, but rather as one with multiple sets of cultural beliefs, values, and behaviors. In particular, we noted a behavior among students we’ve called Swing Staters, who expressed meritocratic beliefs, yet, who we argue, may be critical to reducing bias in engineering education. These findings, analyzed along interwoven threads of race and gender, demonstrate the efficacy of the Articulating a Succinct Description method and contribute to efforts in engineering education to advance pedagogical tools to reduce bias and exclusions in these fields.