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1. Documenting the Unwritten Curriculum of Student Research

   Wilson, Shomir (January 2024, Association for Computational Linguistics)

   Graduate and undergraduate student researchers in natural language processing (NLP) often need mentoring to learn the norms of research. While methodological and technical knowledge are essential, there is also a “hidden curriculum” of experiential knowledge about topics like work strategies, common obstacles, collaboration, conferences, and scholarly writing. As a professor, I have written a set of guides that cover typically unwritten customs and procedures for academic research. I share them with advisees to help them understand research norms and to help us focus on their specific questions and interests. This paper describes these guides, which are freely accessible on the web (https://shomir.net/advice), and I provide recommendations to faculty who are interested in creating similar materials for their advisees. 

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2. I Need Your Advice... Human Perceptions of Robot Moral Advising Behaviors

   Starr, Nichole; Malle, Bertram; Williams, Tom (January 2021, TRAITS Workshop at HRI 2021)

   null (Ed.)

   Due to their unique persuasive power, language-capable robots must be able to both act in line with human moral norms and clearly and appropriately communicate those norms. These requirements are complicated by the possibility that humans may ascribe blame differently to humans and robots. In this work, we explore how robots should communicate in moral advising scenarios, in which the norms they are expected to follow (in a moral dilemma scenario) may be different from those their advisees are expected to follow. Our results suggest that, in fact, both humans and robots are judged more positively when they provide the advice that favors the common good over an individual’s life. These results raise critical new questions regarding people’s moral responses to robots and the design of autonomous moral agents. 

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3. Sheltering: Care Tactics for Ethnography Attentive to Intersectionality and Underrepresentation in Technoscience

   https://doi.org/10.28968/cftt.v9i2.39368  

   Carrigan, Coleen (November 2023, Catalyst: Feminism, Theory, Technoscience)

   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. 

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4. How Data Scientists Review the Scholarly Literature

   https://doi.org/10.1145/3576840  

   Mysore, Sheshera; Jasim, Mahmood; Song, Haoru; Akbar, Sarah; Randall, Andre Kenneth; Mahyar, Narges (March 2023, ACM)

   Gwizdka, Jacek; Rieh, Soo Young (Ed.)

   Keeping up with the research literature plays an important role in the workflow of scientists – allowing them to understand a field, formulate the problems they focus on, and develop the solutions that they contribute, which in turn shape the nature of the discipline. In this paper, we examine the literature review practices of data scientists. Data science represents a field seeing an exponential rise in papers, and increasingly drawing on and being applied in numerous diverse disciplines. Recent efforts have seen the development of several tools intended to help data scientists cope with a deluge of research and coordinated efforts to develop AI tools intended to uncover the research frontier. Despite these trends indicative of the information overload faced by data scientists, no prior work has examined the specific practices and challenges faced by these scientists in an interdisciplinary field with evolving scholarly norms. In this paper, we close this gap through a set of semi-structured interviews and think-aloud protocols of industry and academic data scientists (N = 20). Our results while corroborating other knowledge workers’ practices uncover several novel findings: individuals (1) are challenged in seeking and sensemaking of papers beyond their disciplinary bubbles, (2) struggle to understand papers in the face of missing details and mathematical content, (3) grapple with the deluge by leveraging the knowledge context in code, blogs, and talks, and (4) lean on their peers online and in-person. Furthermore, we outline future directions likely to help data scientists cope with the burgeoning research literature. 

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5. The Changing Landscape of Doctoral Education in Science, Technology, Engineering, and Mathematics: PhD Students, Faculty Advisors, and Preferences for Varied Career Options

   https://doi.org/10.3389/fpsyg.2021.711615  

   Sherman, David K.; Ortosky, Lauren; Leong, Suyi; Kello, Christopher; Hegarty, Mary (December 2021, Frontiers in Psychology)

   The landscape of graduate science education is changing as efforts to diversify the professoriate have increased because academic faculty jobs at universities have grown scarce and more competitive. With this context as a backdrop, the present research examines the perceptions and career goals of advisors and advisees through surveys of PhD students (Study 1, N  = 195) and faculty mentors (Study 2, N  = 272) in science, technology, engineering, and math disciplines. Study 1 examined actual preferences and career goals of PhD students among three options: research careers, teaching careers, and non-academic careers in industry, and compared the actual preferences of students with what they perceived as being the normative preferences of faculty. Overall, students had mixed preferences but perceived that their advisors had a strong normative preference for research careers for them. Moreover, students who ranked research positions as most desirable felt the most belonging in their academic departments. Further analyses revealed no differences in career preferences as a function of underrepresented minority (URM) student status or first-generation (FG) status, but URM and FG students felt less belonging in their academic departments. Study 2 examined faculty preferences for different careers for their advisees, both in general and for current students in particular. While faculty advisors preferred students to go into research in general, when focusing on specific students, they saw their preferences as being closely aligned with the career preference of each PhD student. Faculty advisors did not perceive any difference in belonging between their students as a function of their URM status. Discrepancies between student and faculty perceptions may occur, in part, because faculty and students do not engage in sufficient discussions about the wider range of career options beyond academic research. Supporting this possibility, PhD students and faculty advisors reported feeling more comfortable discussing research careers with each other than either non-academic industry positions or teaching positions. Discussion centers on the implications of these findings for interpersonal and institutional efforts to foster diversity in the professoriate and to create open communication about career development. 

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