Can machines perform a qualitative data analysis? Reading the debate with Alan Turing
Fuente:
arXiv
Saved in:
| Main Author: | De Paoli, Stefano |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Codebook Reduction and Saturation: Novel observations on Inductive Thematic Saturation for Large Language Models and initial coding in Thematic Analysis
by: De Paoli, Stefano, et al.
Published: (2025)
by: De Paoli, Stefano, et al.
Published: (2025)
Attributions toward Artificial Agents in a modified Moral Turing Test
by: Aharoni, Eyal, et al.
Published: (2024)
by: Aharoni, Eyal, et al.
Published: (2024)
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
by: La Cava, Lucio, et al.
Published: (2025)
by: La Cava, Lucio, et al.
Published: (2025)
Must Read: A Comprehensive Survey of Computational Persuasion
by: Bozdag, Nimet Beyza, et al.
Published: (2025)
by: Bozdag, Nimet Beyza, et al.
Published: (2025)
Reflections on Inductive Thematic Saturation as a potential metric for measuring the validity of an inductive Thematic Analysis with LLMs
by: De Paoli, Stefano, et al.
Published: (2024)
by: De Paoli, Stefano, et al.
Published: (2024)
Passed the Turing Test: Living in Turing Futures
by: Gonçalves, Bernardo
Published: (2024)
by: Gonçalves, Bernardo
Published: (2024)
Societal Alignment Frameworks Can Improve LLM Alignment
by: Stańczak, Karolina, et al.
Published: (2025)
by: Stańczak, Karolina, et al.
Published: (2025)
Empathy and the Right to Be an Exception: What LLMs Can and Cannot Do
by: Kidder, William, et al.
Published: (2024)
by: Kidder, William, et al.
Published: (2024)
Can Large Language Models Replace Human Coders? Introducing ContentBench
by: Haman, Michael
Published: (2026)
by: Haman, Michael
Published: (2026)
Can LLMs make trade-offs involving stipulated pain and pleasure states?
by: Keeling, Geoff, et al.
Published: (2024)
by: Keeling, Geoff, et al.
Published: (2024)
Does GPT-4 surpass human performance in linguistic pragmatics?
by: Bojic, Ljubisa, et al.
Published: (2023)
by: Bojic, Ljubisa, et al.
Published: (2023)
Large Language Models Can Be a Viable Substitute for Expert Political Surveys When a Shock Disrupts Traditional Measurement Approaches
by: Wu, Patrick Y.
Published: (2025)
by: Wu, Patrick Y.
Published: (2025)
Why Machines Can't Be Moral: Turing's Halting Problem and the Moral Limits of Artificial Intelligence
by: Passamonti, Massimo
Published: (2024)
by: Passamonti, Massimo
Published: (2024)
System 2 thinking in OpenAI's o1-preview model: Near-perfect performance on a mathematics exam
by: de Winter, Joost, et al.
Published: (2024)
by: de Winter, Joost, et al.
Published: (2024)
Can LLMs Think Like Consumers? Benchmarking Crowd-Level Reaction Reconstruction with ConsumerSimBench
by: Wang, Tianyu, et al.
Published: (2026)
by: Wang, Tianyu, et al.
Published: (2026)
Can LLMs Estimate Student Struggles? Human-AI Difficulty Alignment with Proficiency Simulation for Item Difficulty Prediction
by: Li, Ming, et al.
Published: (2025)
by: Li, Ming, et al.
Published: (2025)
Guided Persona-based AI Surveys: Can we replicate personal mobility preferences at scale using LLMs?
by: Tzachristas, Ioannis, et al.
Published: (2025)
by: Tzachristas, Ioannis, et al.
Published: (2025)
No Free Lunch in Language Model Bias Mitigation? Targeted Bias Reduction Can Exacerbate Unmitigated LLM Biases
by: Chand, Shireen, et al.
Published: (2025)
by: Chand, Shireen, et al.
Published: (2025)
Noosemia: toward a Cognitive and Phenomenological Account of Intentionality Attribution in Human-Generative AI Interaction
by: De Santis, Enrico, et al.
Published: (2025)
by: De Santis, Enrico, et al.
Published: (2025)
neuralFOMO: Can LLMs Handle Being Second Best? Measuring Envy-Like Preferences in Multi-Agent Settings
by: Ramamoorthy, Arnav, et al.
Published: (2025)
by: Ramamoorthy, Arnav, et al.
Published: (2025)
Can Large Language Models Simulate Human Responses? A Case Study of Stated Preference Experiments in the Context of Heating-related Choices
by: Wang, Han, et al.
Published: (2025)
by: Wang, Han, et al.
Published: (2025)
Open Source Language Models Can Provide Feedback: Evaluating LLMs' Ability to Help Students Using GPT-4-As-A-Judge
by: Koutcheme, Charles, et al.
Published: (2024)
by: Koutcheme, Charles, et al.
Published: (2024)
The Polite Liar: Epistemic Pathology in Language Models
by: DeVilling, Bentley
Published: (2025)
by: DeVilling, Bentley
Published: (2025)
The Collective Turing Test: Large Language Models Can Generate Realistic Multi-User Discussions
by: Bouleimen, Azza, et al.
Published: (2025)
by: Bouleimen, Azza, et al.
Published: (2025)
Turing's Test, a Beautiful Thought Experiment
by: Gonçalves, Bernardo
Published: (2023)
by: Gonçalves, Bernardo
Published: (2023)
StereoTales: A Multilingual Framework for Open-Ended Stereotype Discovery in LLMs
by: Jeune, Pierre Le, et al.
Published: (2026)
by: Jeune, Pierre Le, et al.
Published: (2026)
Conformity and Social Impact on AI Agents
by: Bellina, Alessandro, et al.
Published: (2026)
by: Bellina, Alessandro, et al.
Published: (2026)
A Close Reading Approach to Gender Narrative Biases in AI-Generated Stories
by: Raffini, Daniel, et al.
Published: (2025)
by: Raffini, Daniel, et al.
Published: (2025)
An analysis of AI Decision under Risk: Prospect theory emerges in Large Language Models
by: Payne, Kenneth
Published: (2025)
by: Payne, Kenneth
Published: (2025)
Can adversarial attacks by large language models be attributed?
by: Cebrian, Manuel, et al.
Published: (2024)
by: Cebrian, Manuel, et al.
Published: (2024)
AI-Generated Slides: Are They Good? Can Students Tell?
by: Leinonen, Juho, et al.
Published: (2026)
by: Leinonen, Juho, et al.
Published: (2026)
Comparative analysis of privacy-preserving open-source LLMs regarding extraction of diagnostic information from clinical CMR imaging reports
by: Amirrajab, Sina, et al.
Published: (2025)
by: Amirrajab, Sina, et al.
Published: (2025)
"I Am the One and Only, Your Cyber BFF": Understanding the Impact of GenAI Requires Understanding the Impact of Anthropomorphic AI
by: Cheng, Myra, et al.
Published: (2024)
by: Cheng, Myra, et al.
Published: (2024)
Anticipating Innovation Using Large Language Models
by: Fenoaltea, Enrico Maria, et al.
Published: (2026)
by: Fenoaltea, Enrico Maria, et al.
Published: (2026)
AI-Driven Automation Can Become the Foundation of Next-Era Science of Science Research
by: Chen, Renqi, et al.
Published: (2025)
by: Chen, Renqi, et al.
Published: (2025)
What About the Scene with the Hitler Reference? HAUNT: A Framework to Probe LLMs' Self-consistency Via Adversarial Nudge
by: Dutta, Arka, et al.
Published: (2025)
by: Dutta, Arka, et al.
Published: (2025)
How word semantics and phonology affect handwriting of Alzheimer's patients: a machine learning based analysis
by: Cilia, Nicole Dalia, et al.
Published: (2023)
by: Cilia, Nicole Dalia, et al.
Published: (2023)
The Imitation Game According To Turing
by: Temtsin, Sharon, et al.
Published: (2025)
by: Temtsin, Sharon, et al.
Published: (2025)
Can Large Language Models Understand As Well As Apply Patent Regulations to Pass a Hands-On Patent Attorney Test?
by: Khera, Bhakti, et al.
Published: (2025)
by: Khera, Bhakti, et al.
Published: (2025)
Lived Experience Not Found: LLMs Struggle to Align with Experts on Addressing Adverse Drug Reactions from Psychiatric Medication Use
by: Chandra, Mohit, et al.
Published: (2024)
by: Chandra, Mohit, et al.
Published: (2024)
Similar Items
-
Codebook Reduction and Saturation: Novel observations on Inductive Thematic Saturation for Large Language Models and initial coding in Thematic Analysis
by: De Paoli, Stefano, et al.
Published: (2025) -
Attributions toward Artificial Agents in a modified Moral Turing Test
by: Aharoni, Eyal, et al.
Published: (2024) -
OpenTuringBench: An Open-Model-based Benchmark and Framework for Machine-Generated Text Detection and Attribution
by: La Cava, Lucio, et al.
Published: (2025) -
Must Read: A Comprehensive Survey of Computational Persuasion
by: Bozdag, Nimet Beyza, et al.
Published: (2025) -
Reflections on Inductive Thematic Saturation as a potential metric for measuring the validity of an inductive Thematic Analysis with LLMs
by: De Paoli, Stefano, et al.
Published: (2024)