Date of Award
May 2026
Document Type
Dissertation
Degree Name
Doctor of Philosophy (PhD)
Department
Earth System Science & Policy
First Advisor
Rebecca Romsdahl
Abstract
This dissertation examined how environmental frames—particularly “Mother Earth” and “Mother Nature”—influence public support for climate policy and how these effects differ across demographic groups. Using a mixed-methods explanatory sequential design, the study executed a media survey experiment (N = 470) followed by semi-structured expert interviews to assess responses to two primes using a conventional corporate media message, and a message with similar content that was generated by Artificial Intelligence (AI).
Quantitative findings showed that these metaphors generated uneven and sometimes divergent priming effects. Some groups—most notably White male Republicans—reported higher support for environmental policy under the conventional media prime. Under the same conventional prime, other demographic groups expressed ambivalence or reduced engagement likely tied to representational fatigue or stereotype activation. Qualitative interviews contextualized these patterns by highlighting the challenges science communicators face creating resonance, especially in crafting messages in an environment where AI plays a growing role in shaping public narratives.
As a theoretical contribution, this research introduces a Resonance Consistency Framework. The framework explains how identity cueing and interpretive anchoring interact to shape whether a message produces shared, stable policy meaning—or divergent, audience-specific readings. Overall, the findings demonstrate that gendered environmental metaphors remain powerful but uneven tools in climate communication, offering guidance for communicators and policymakers seeking to design messages that better anticipate audience-specific interpretations.
Recommended Citation
Colosimo, Jayme, "Climate Messages In An AI-Integrated Experiment: Interpretive Divergence, Policy Support, And A New Resonance Consistency Framework" (2026). Theses and Dissertations. 13936.
https://commons.und.edu/theses/13936