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25th IEEE International Conference on Information Reuse and Integration for Data Science (IEEE IRI)

作者： Zhao, Fengxiang Yu, Fan Shang, Yi Univ Missouri Dept Elect Engn & Comp Sci Columbia MO 65211 USA Univ Missouri Sch Informat Sci & Learning Techn Columbia MO USA

ISBN: (纸本)9798350351194;9798350351187

Recent advances in Large Language Models (LLMs) have revolutionized numerous fields, including Qualitative Data Analysis (QDA). This paper introduces a novel method, ARGUMENT2CODE (A2C), designed to leverage the capabilities of LLMs for enhancing the QDA process, particularly focusing on the inductive coding aspect. A2C sets itself apart from conventional automated coding tools by initiating a two-stage fine-tuned LLM process adept at navigating the complex landscape of qualitative data. This innovative method starts with the identification of coding cues hidden within the textual data, which are then refined into targeted prompts. These prompts are then used for guiding the inductive coding process, facilitating the generation of a rich, actionable codebook that offers expansive coverage of analytical perspectives. Our experimentation reveals that A2C not only successfully generates a pertinent and insightful codebook consistently but also significantly outperforms all other existing methods.

关键词： Qualitative Data Analysis inductive coding Large Language Models (LLMs) Textual Data Analysis Thematic Analysis

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Security Best Practices: A Critical Analysis Using IoT as a Case Study

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ACM TRANSACTIONS ON PRIVACY AND SECURITY 2023年第2期26卷 1-30页

作者： Barrera, David Bellman, Christopher Van Oorschot, Paul Carleton Univ 1125 Colonel By Dr Ottawa ON K1S 5B6 Canada

Academic research has highlighted the failure of many Internet of Things (IoT) product manufacturers to follow accepted practices, while IoT security best practices have recently attracted considerable attention worldwide from industry and governments. Given current examples of security advice, confusion is evident from guidelines that conflate desired outcomes with security practices to achieve those outcomes. We explore a surprising lack of clarity, and void in the literature, on what (generically) best practice means, independent of identifying specific individual practices or highlighting failure to follow best practices. We consider categories of security advice, and analyze how they apply over the lifecycle of IoT devices. For concreteness in discussion, we use iterative inductive coding to code and systematically analyze a set of 1,013 IoT security best practices, recommendations, and guidelines collated from industrial, government, and academic sources. Among our findings, of all analyzed items, 68% fail to meet our definition of an (actionable) practice, and 73% of all actionable advice relates to the software development lifecycle phase, highlighting the critical position of manufacturers and developers. We hope that our work provides a basis for the community to better understand best practices, identify and reach consensus on specific practices, and find ways to motivate relevant stakeholders to follow them.

关键词： IoT security best practices security advice inductive coding device lifecycle

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ChatGPT for Education Research: Exploring the Potential of Large Language Models for Qualitative Codebook Development 25th

ChatGPT for Education Research: Exploring the Potential of L...

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25th International Conference on Artificial Intelligence in Education (AIED)

作者： Barany, Amanda Nasiar, Nidhi Porter, Chelsea Zambrano, Andres Felipe Andres, Alexandra L. Bright, Dara Shah, Mamta Liu, Xiner Gao, Sabrina Zhang, Jiayi Mehta, Shruti Choi, Jaeyoon Giordano, Camille Baker, Ryan S. Univ Penn Philadelphia PA 19104 USA Consortium DEI Hlth Educators Philadelphia PA USA Elsevier Philadelphia PA 19103 USA Univ Wisconsin Madison WI 53706 USA

ISBN: (纸本)9783031642982;9783031642999

In qualitative data analysis, codebooks offer a systematic framework for establishing shared interpretations of themes and patterns. While the utility of codebooks is well-established in educational research, the manual process of developing and refining codes that emerge bottom-up from data presents a challenge in terms of time, effort, and potential for human error. This paper explores the potentially transformative role that could be played by Large Language Models (LLMs), specifically ChatGPT (GPT-4), in addressing these challenges by automating aspects of the codebook development process. We compare four approaches to codebook development - a fully manual approach, a fully automated approach, and two approaches that leverage ChatGPT within specific steps of the codebook development process. We do so in the context of studying transcripts from math tutoring lessons. The resultant four codebooks were evaluated in terms of whether the codes could reliably be applied to data by human coders, in terms of the human-rated quality of codes and codebooks, and whether different approaches yielded similar or overlapping codes. The results show that approaches that automate early stages of codebook development take less time to complete overall. Hybrid approaches (whether GPT participates early or late in the process) produce codebooks that can be applied more reliably and were rated as better quality by humans. Hybrid approaches and a fully human approach produce similar codebooks;the fully automated approach was an outlier. Findings indicate that ChatGPT can be valuable for improving qualitative codebooks for use in AIED research, but human participation is still essential.

关键词： Large Language Models ChatGPT inductive coding Research Methods

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An empirical investigation of command-line customization

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EMPIRICAL SOFTWARE ENGINEERING 2022年第2期27卷 1-26页

作者： Schroeder, Michael Cito, Juergen TU Wien Vienna Austria MIT 77 Massachusetts Ave Cambridge MA 02139 USA

The interactive command line, also known as the shell, is a prominent mechanism used extensively by a wide range of software professionals (engineers, system administrators, data scientists, etc.). Shell customizations can therefore provide insight into the tasks they repeatedly perform, how well the standard environment supports those tasks, and ways in which the environment could be productively extended or modified. To characterize the patterns and complexities of command-line customization, we mined the collective knowledge of command-line users by analyzing more than 2.2 million shell alias definitions found on GitHub. Shell aliases allow command-line users to customize their environment by defining arbitrarily complex command substitutions. Using inductive coding methods, we found three types of aliases that each enable a number of customization practices: Shortcuts (for nicknaming commands, abbreviating subcommands, and bookmarking locations), Modifications (for substituting commands, overriding defaults, colorizing output, and elevating privilege), and Scripts (for transforming data and chaining subcommands). We conjecture that identifying common customization practices can point to particular usability issues within command-line programs, and that a deeper understanding of these practices can support researchers and tool developers in designing better user experiences. In addition to our analysis, we provide an extensive reproducibility package in the form of a curated dataset together with well-documented computational notebooks enabling further knowledge discovery and a basis for learning approaches to improve command-line workflows.

关键词： Command line Customization practices Collective knowledge inductive coding

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Unveiling Hidden Stories: Automated Narrative Extraction from Holocaust Diaries with Ensemble LLMs 8

Unveiling Hidden Stories: Automated Narrative Extraction fro...

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8th International Workshop on Narrative Extraction From Texts, Text2Story 2025

作者： Parfenova, Angelina Technical University of Munich Munich Germany Lucerne University of Applied Sciences and Arts Rotkreuz Switzerland

This paper presents a novel application of ensemble-based large language models (LLMs) with Retrieval-Augmented Generation (RAG) for automated inductive coding of Holocaust children’s diaries. Our approach integrates multiple smaller LLMs, fine-tuned via Low-Rank Adaptation (LoRA), and employs a moderator-based mechanism to simulate collaborative human consensus. We evaluate our best model on a curated dataset of diaries, demonstrating significant improvements in coding consistency and specificity. Our results highlight the potential of ensemble-based LLMs with RAG for analyzing sensitive historical texts, offering a scalable and efficient alternative to manual coding while preserving the nuanced emotional and thematic content of the diaries. © 2025 Copyright for this paper by its authors.

关键词： Ensemble models Holocaust diaries inductive coding Qualitative analysis Retrieval-Augmented Generation

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Health Behavior Change Processes Among Adults With Serious Mental Illness Engaged in Illness Self-Management

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QUALITATIVE HEALTH RESEARCH 2021年第6期31卷 1155-1168页

作者： Muralidharan, Anjana Peeples, Amanda Lucksted, Alicia VA Capitol Healthcare Network VISN 5 Mental Illness Res Educ & Clin Ctr MIRECC 10 N Greene St Baltimore MD 21201 USA Univ Maryland Sch Med Baltimore MD USA Univ Maryland Sch Med Dept Psychiat Baltimore MD USA

Self-management interventions promote illness management among adults with chronic health conditions. Little is known regarding the processes by which these interventions have their effects. The present study examined how Living Well, an effective self-management intervention for adults with serious mental illness, led to health behavior change in a randomized controlled trial. A convenience subset (N = 15) of participants completed qualitative interviews regarding the feasibility/acceptability of Living Well. An inductive secondary qualitative analysis, using a combination of interpretive phenomenological and social constructivist approaches, was conducted to examine processes of change. Results indicate that Living Well provided information and knowledge, opportunities for learning from others and real-world practice, and an interpersonally supportive environment. These active ingredients led to enhanced self-awareness, confidence, sense of control, and behavior and health status changes among participants. These findings are considered in the context of prominent behavior change theories such as social cognitive theory and self-regulation.

关键词： self-management health behavior change serious mental illness qualitative inductive coding Mid-Atlantic United States

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Remote and Equitable inductive Analysis for Global Health Teams: Using Digital Tools to Foster Equity and Collaboration in Qualitative Global Health Research via the R-EIGHT Method

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INTERNATIONAL JOURNAL OF QUALITATIVE METHODS 2024年 23卷

作者： Johnson-Peretz, Jason Arunga, Titus O. Lee, Joi Akatukwasa, Cecilia Atwine, Fredrick Onyango, Angeline Owino, Lawrence Camlin, Carol S. Univ Calif San Francisco UCSF Dept Obstet Gynecol & Reproduct Sci San Francisco CA USA Kenya Med Res Inst KEMRI Kisumu Kenya Infect Dis Res Collaborat Kampala Uganda Univ Calif San Francisco UCSF Dept Med San Francisco CA USA Dept Obstet Gynecol & Reproduct Sci Univ Calif San Francisco Mission Hall Global Hlth & Clin Sci Bldg550 16th San Francisco CA 94143 USA

Qualitative methods encompass a variety of research and analysis techniques which have the common aim of uncovering what cannot be captured numerically through the quantification of data. For qualitative analytical methods in the interpretivist tradition (e.g. grounded theory, phenomenological, thematic, etc), inductive coding has become a mainstay but has not always lent itself to collaborative, remote team-based data interpretation among qualitative and mixed-methods clinical researchers. Finding ways to speed the inductive coding process without sacrificing rigour while remaining accessible to geographically dispersed teams remains a priority. This is especially crucial in global health partnerships where on-the-ground researchers may have less input into codebook development compared to in-the-office researchers. We describe a newly-developed, digital approach that integrates findings from our qualitative team, which we call R-EIGHT (Remote and Equitable inductive Analysis for Global Health Teams). The technique we developed a) speeds the process of inductive coding as a team, b) visually displays interpretive consensus, and c) when appropriate fosters streamlined integration of inductive findings into codebooks. Because it involves all team members, our approach helps break the divide between in-office and on-the-ground teams, fostering integrated and representative contributions from all globally-dispersed team members.

关键词： qualitative methods remote teams global health inductive coding equity

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Comparing thematic and search term-based coding in understanding sense of place in survey research

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JOURNAL OF ENVIRONMENTAL PSYCHOLOGY 2024年 96卷

作者： Cotton, Isabel McWherter, Brooke Tenbrink, Thora Sherren, Kate Dalhousie Univ Sch Resource & Environm Studies Halifax NS Canada Univ East Anglia Tyndall Ctr Climate Change Res Norwich England Bangor Univ Sch Arts Culture & Language Bangor Wales Univ East Anglia Sch Global Dev Zuckerman BldgNorwich Res Pk Norwich NR4 7TJ England

Sense of place is a fundamental concept in human geography, yet challenging to measure given its intangibility and idiosyncrasy. Meanwhile, there are increasing opportunities for social scientists to utilize big data and automated approaches to data analysis, albeit with some wariness, but few researchers directly compare automated to manual analysis in the context of sense of place. This study applies two analytical approaches to a survey question on sense of place: semi-automatic search term analysis around semantic fields, and inductive thematic analysis. Results show high agreement between the approaches, with more tangible aspects of place (recreation) better correlated than more abstract concepts (appreciation). Variation mainly relates to the ability of inductive coding to address false negatives, implied meaning, or obscure search terms. This demonstrates the potential value of hybridizing to improve the accuracy of a search term-based approach, and overcome the limitations, such as subjectivities, of one analytical approach.

关键词： Sense of place Place attachment Cognitive discourse analysis inductive coding Qualitative analysis Thematic analysis

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Journalism Graduates’ Perceptions and Experiences of Internships and Employment

Journalism Graduates’ Perceptions and Experiences of Intern...

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作者： Ahearn, Breanne L. Walden University

学位级别：Ed.D., Doctor of Education

Graduates who do not have an internship show greater difficulty in obtaining gainful employment compared to those who do have an internship. However, simply believing that internships may translate into employment does not explain how students perceive the role of the internship in obtaining employment in the field. The purpose of the study was to investigate the graduates’ perceptions and experiences of how internships might affect journalism majors’ gaining employment in the field within 6 months of graduation. Kolb’s four stages of experiential learning theory was used as the conceptual framework to investigate how journalism-related major graduates described their journalism internship perceptions and experiences that led to employment in the field. A qualitative phenomenographical research design was used to conduct 10 semistructured, interviews using a video platform with participants who had completed at least one journalism internship and had obtained a job in the field of journalism or a related field within 6 months of graduating. Using inductive coding, themes identified were participants perceived internships strengthened skills development and affective behavior development. Subthemes included the perceived development of (a) technical skills, (b) production skills, and (c) interviewing skills. Study findings were that graduates’ journalism skills were solidified beyond learning they experienced in traditional university classrooms, consequently leading to swift employment in competitive positions in the field of journalism. These findings may inform university stakeholders to optimize the use of student internships thereby advancing graduates to the forefront of the journalism field.

关键词： Internships inductive coding Technical skills Production skills Interviewing skills

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Applying Thematic Analysis to Education: A Hybrid Approach to Interpreting Data in Practitioner Research

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INTERNATIONAL JOURNAL OF QUALITATIVE METHODS 2020年 19卷 1-9页

作者： Xu, Wen Zammit, Katina Western Sydney Univ Second Ave Penrith NSW 2747 Australia

Thematic analysis (TA), as a qualitative analytic method, is widely used in health care, psychology, and beyond. However, scant details are often given to demonstrate the process of data analysis, especially in the field of education. This article describes how a hybrid approach of TA was applied to interpret multiple data sources in a practitioner inquiry. Particular attention is given to the inductive and deductive coding and theme development process of TA. Underpinned by the constructivist epistemology, codes were driven by both data per se and theories, through a "bottom-up" and "top-down" approach to identify themes. A detailed example of six steps of data analysis is presented, which evidences the systematic analysis of raw data from observation and research journals, students' focus groups, and a classroom teacher's semistructured interviews. This example demonstrates how classroom practice was unpacked and how insiders' insights were interpreted through the theoretical lens while also allowing the participants to express themselves. By providing step-by-step guidelines in data coding and identification of themes, this article contributes to informing qualitative researchers, especially teacher-researchers who undertake their research in the classroom setting.

关键词： qualitative research thematic analysis practitioner research inductive coding deductive coding

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