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This paper's focus is on the "middle" of partnerships for equity in science education. Middle is used in a temporal sense, meaning the time after the general purposes and terms of working together have been set and before outcomes have been achieved. The middle of the partnership also represents people interacting, bounded around the edges, by their institutional roles, norms, resources, and priorities. As co-authors who had not previously collaborated (or even met in person), we approached the construction of this manuscript as a dialogue where we learn by sharing narrations of experiences and values and principles. We were inspired by the conversational book between Horton and Freire (1990) and specifically their discussion of "Is it possible to just teach biology?" (p. 102). In our conversation, we illustrate the ways in which partnerships may make justice-oriented science education possible. Our focus is on complementary and contradictory knowledges, and ways of knowing, institutional resources and constraints, and strategies for making transformative change. We explore the middle of partnerships as a series of opportunities for learning and growing, caring for one another, and building solidarity spaces together.

Assessment developers are increasingly using the developing technology of machine learning in transforming how to assess students in their science learning. I argue that these algorithmic models further embed the structures of inequality that are pervasive in the development of science assessments in how they legitimize certain language practices that protect the hierarchical standing of status quo interests. My argument is situated within the broader emerging ethical challenges around this new technology. I apply a raciolinguistic equity analysis framework in critiquing the "new black box" that reinforces structural forms of discrimination against the linguistic repertoires of racially marginalized student populations. The article ends with me sharing a set of tactical shifts that can be deployed to form a more equitable and socially-just field of machine learning enhanced science assessments.