From Protest to Policy: An AI-Assisted Meta-Analytic Journey Through LGBTQ+ Change

Abstract

This dissertation examines how activism, public opinion, and legal change interact in the advancement of LGBTQ+ rights. A human-in-the-loop, AI-assisted meta-analytic workflow employed ASReview LAB, Elicit AI, and GPT to support screening, data extraction, and validation. The final synthesis included 28 effects represented in 25 independent study matrices with a summed analytic sample size of N=1,613,593. Random-effects meta-analytic structural equation modeling (MASEM) was used to pool associations among the three constructs and compare competing conceptual models of sociopolitical change. Activism and public opinion were each positively associated with policy or legal change, whereas the direct association between activism and public opinion was small and nonsignificant. The strongest-fitting model was the policy-responsive mobilization model (Model 9; CFI= 1.00, TLI= 1.278, AIC= -1.98, BIC=-14.27). In this model, public opinion predicts policy or legal change (b = 0.186), which subsequently predicts activism (b = 0.187). However, the indirect effect was not statistically significant, so the findings do not confirm mediation or causality. The public opinion-policy relationship was the most stable across sensitivity and influence analyses. Nevertheless, the evidence base was highly heterogeneous, temporally constrained to the 21st century, concentrated in the Global North, and unevenly distributed across relationships. Overall, the findings suggest that legal reform may function as both an outcome of social movements and as a source of legitimacy and opportunity for renewed activism.