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Following the Dobbs v. Jackson Women’s Health Organization decision, Republican-controlled legislatures across the U.S. initiated draconian abortion restrictions. In order to appeal to anti-abortion policymakers, advocates across the country have strategically separated “maternal and child health” (MCH) issues, such as increased insurance coverage for midwifery and doula care, from issues often labeled as “reproductive rights,” such as access to sex education, birth control, and abortion. Advocates point out this strategic separation has likely contributed overall to the downfall of abortion rights. In this paper, we analyze legislative discourse to understand the legislative challenges advocates face, the strategic separations and allyships they employ, and the implications for other states and reproductive health more broadly. We find that legislators legitimate the same scientific evidence in some contexts while not in others in order to hold onto rhetorical purity within the abortion debate. In their attempts to parse the ideal abortion seeker, conservative legislators create legal ambiguities with serious consequences for healthcare.

This assignment asks first-year writing students to collaboratively create a tabletop game design that would expose players to possible future developments of climate change. The multimodal component is accompanied by a series of writing, research, and communication assignments that are scaffolded to guide students through the iterative process of composing. As students explore alternative formats to make a persuasive argument, they gain a more nuanced understanding of their topic, hone critical thinking skills, and practice addressing different audiences. The final project includes a research paper and a formal project proposal. The author argues in favor of the affordances of game-based pedagogies in a writing and technical communication classroom.

Big data technology is gaining a lot of attention and research in the present day. In this article, based on big data technology, we use Spark’s big data hybrid computing model to promote family style and family education under the premise of large-scale information processing. The minimum average distance of all clusters is calculated by computing the mean and eigenvectors of the Hopkins statistic. The cohesiveness and separation of the contour coefficients on the clusters were evaluated based on the mean values. We also examine the error-squared and criterion functions and use this method to verify the positive effect of promoting family traditions and education on the minds of young people. In this paper, we get from the comparison experiment of mining algorithms: Spark algorithm mining efficiency is 200-300 higher than MR algorithm mining efficiency, and the mining efficiency is superior. Especially when the log volume is large, the efficiency enhancement effect is as high as 96.88%, which is conducive to creating a good ideological and political education environment for young people by further improving the positive role of propagating family style and family education in the ideological and political education of young people.