Date of Award

2025

Document Type

Dissertation

Degree Name

Multi-sector Communications (Ph.D.)

Department

Division of Mass Communication

First Advisor

Basilio Monteiro

Second Advisor

Mark Juszczak

Third Advisor

Giancarlo Crocetti

Abstract

This research examines the intersection of media, artificial intelligence, and consciousness through an ethnographic analysis spanning 1940-2024. The study introduces the concept of "Darrative" as a framework for understanding how consciousness processes information, challenging traditional rational choice theory while extending beyond Fisher's narrative paradigm. A key contribution is the development of the "Thinking System" model, demonstrating how information processing occurs through darrative frameworks. The methodology employs Latour's actor-network theory to examine how scientific knowledge is constructed and stabilized through networks of human and non-human actors. Using a systematic Boolean search through academic databases, the study initially identified 15 relevant peer-reviewed articles (2020-2024), later expanding to include works from 1940-2024 due to methodological limitations. The analysis reveals four distinct research epochs: the Foundational Period (1940-1979), characterized by computational frameworks and binary approaches; the Integration Period (1980-1999), marked by neural network models and cognitive theories; the Contemporary Period (2000-2015), featuring sophisticated empirical approaches; and the Current Period (2015-2024), reflecting convergence of multiple research streams and artificial consciousness studies. A significant finding suggests that information serves as the fundamental nexus connecting media, consciousness, and AI. The study develops a metaphor comparing information management to food production and consumption systems, proposing similar regulatory frameworks and quality controls. This analogy encompasses the evolution from local to mass production of information, quality control mechanisms, "information diets", and potential "information pollution." The research applies Latour's ethnographic approach to analyze information production, labeling, distribution, and consumption, revealing complex networks of human and non-human actors. The findings have important implications for developing regulatory frameworks for AI and media technologies, including the need for "information labels" and quality indicators. The study concludes that information consumption, like food consumption, significantly influences collective consciousness and political dialogue, necessitating careful consideration of regulatory approaches in an increasingly AI-mediated world.different disciplines.

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