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1. Improving research integrity: a framework for responsible science communication

   https://doi.org/10.1186/s13104-022-06065-5  

   Ciubotariu, Ilinca I.; Bosch, Gundula (December 2022, BMC Research Notes)

   Abstract Research integrity, an essential precept of scientific inquiry and discovery, comprises norms such as Rigor, Reproducibility, and Responsibility (the 3R’s). Over the past decades, numerous issues have arisen that challenge the reliability of scientific studies, including irreproducibility crises, lack of good scientific principles, and erroneous communications, which have impacted the public’s trust in science and its findings. Here, we highlight one important component of research integrity that is often overlooked in the discussion of proposals for improving research quality and promoting robust research; one that spans from the lab bench to the dissemination of scientific work: responsible science communication. We briefly outline the role of education and institutions of higher education in teaching the tenets of good scientific practice and within that, the importance of adequate communications training. In that context, we present our framework of responsible science communication that we live by and teach to our students in courses and workshops that are part of the Johns Hopkins Bloomberg School of Public Health R 3 Center for Innovation in Science Education. 

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2. Rigor in AI: Doing Rigorous AI Work Requires a Broader, Responsible AI-Informed Conception of Rigor

   Olteanu, Alexandra; Blodgett, Su_Lin; Balayn, Agathe; Wang, Angelina; Diaz, Fernando; du_Pin_Calmon, Flavio; Mitchell, Margaret; Ekstrand, Michael D (December 2025, NeurIPS)

   In AI research and practice, rigor remains largely understood in terms of methodological rigor — such as whether mathematical, statistical, or computational methods are correctly applied. We argue that this narrow conception of rigor has contributed to the concerns raised by the responsible AI community, including overblown claims about AI capabilities. Our position is that a broader conception of what rigorous AI research and practice should entail is needed. We believe such a conception — in addition to a more expansive understanding of (1) methodological rigor — should include aspects related to (2) what background knowledge informs what to work on (epistemic rigor); (3) how disciplinary, community, or personal norms, standards, or beliefs influence the work (normative rigor); (4) how clearly articulated the theoretical constructs under use are (conceptual rigor); (5) what is reported and how (reporting rigor); and (6) how well-supported the inferences from existing evidence are (interpretative rigor). In doing so, we also aim to provide useful language and a framework for much-needed dialogue about the AI community’s work by researchers, policymakers, journalists, and other stakeholders. 

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3. Deconstructing Conceptions of Rigor in Computer Science Education

   https://doi.org/10.1145/3776542  

   Everson, Jayne; Kivuva, F Megumi; Sherif, Eman; Oleson, Alannah; Ko, Amy (November 2025, ACM Transactions on Computing Education)

   ObjectivesIn calls for excellent and equitable Computer Science (CS) education, the wordrigoroften appears, but it often goes undefined. The goal of this work is to understand how CS teachers, instructors, and students conceive of rigor. Research Questions:1) What do CS instructors think rigor is? and 2) What do students think rigor is? Methods:Using the principles of phenomenological research, we conducted a semi-structured interview study with 10 post-secondary CS students, 10 secondary CS teachers, and 9 post-secondary CS instructors, to understand their conceptions of rigor. Results:Analysis showed that no participants had the same understanding of rigor. We found that participants had abstractPrinciples of Rigorwhich included: Precision, Systematic Thought Process, Depth of Understanding, and Challenge. They also had concreteObservations of Rigorthat included Time and Effort, Intrinsic Drive, Productive Failure, Struggle, Outcomes, and Gatekeeping. Participants also sharedConditions for Rigorwhich included Expectations, Standards, Community Support, and Resources. Implications:Our data supports prior work that educators are using different definitions of rigor. This implies that each educator holds different expectations for students, without necessarily communicating these expectations to their students. In the best case, this might confuse students; in the worst case, it reinforces hegemonic norms which can lead to gatekeeping which prevents students from fully participating in the CS field. Based on these insights, we argue that to commit to the idea of quality CS learning, the community must discard the use of this concept of rigor to justify student learning and re-imagine alternate benchmarks. 

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4. Abolitionist Computer Science Teaching: Moving from Access to Justice.

   Ivey, A.; Johnson, S. R.; Skorodinsky, M.; Snyder, J.; & Goode, J. (May 2021, Proceedings of Research in Equity and Sustained Participation in Computing (RESPECT))

   null (Ed.)

   As high school computer science course offerings have expanded over the past decade, gaps in race and gender have remained. This study embraces the “All” in the “CS for All” movement by shifting beyond access and toward abolitionist computer science teaching. Using data from professional development observations and interviews, we lift the voices of BIPOC CS teachers and bring together tenets put forth by Love (2019) for abolitionist teaching along with how these tenets map onto the work occurring in CS classrooms. Our findings indicate the importance of BIPOC teacher representation in CS classrooms and ways abolitionist teaching tenets can inform educator’s efforts at moving beyond broadening participation and toward radical inclusion, educational freedom, and self-determination, for ALL. 

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5. Justice‐centered community–university partnering: Core tenets of partnering for justice epistemology

   https://doi.org/10.1002/sce.21772  

   Luehmann, April (November 2022, Science Education)

   This paper is an introduction to and a synthesis of three papers in this issue written by scholars deeply committed to partnering with communities to understand and enact what it means to realize transformational ends in and through science education. Partnering for justice must be a conversation, a work in progress, and a critical examination that leads to intentional and careful forward movement. It is a beautiful effort at flattening power hierarchies so diverse voices and expertise can be interwoven in service of youth and communities who have been invisibilized and marginalized. Committed to realizing new, hope-filled futures, the three pairs of authors use their experiences and expertise to shed light on the work of partnering using a temporal lens: considerations related to the beginnings, middles, and endings of partnering, each of which requires special intentionality and care. Together the authors share core overlapping tenets with other critical scholars that could be considered a partnering for justice epistemology. This epistemology underscores how importantly different learning through partnering for justice is from traditional notions of academic research. I close the paper by sharing lessons learned from my own 20-plus years of partnering for justice, using the tenets of partnering for justice epistemology as a lens. 

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