Bibliographic record
Abstract
With the advancement of technology, the generation of content, organization and retrieval of information, dissemination of messages, and transmission of knowledge have all evolved into diverse forms and channels. The four academic articles and one practical work report included in this issue precisely reflect the developments in the fields of library and archival studies across these aspects. The key themes of this issue can be glimpsed through the following keywords: resource discovery, curriculum design, news topic analysis, misinformation, and AI policy guidelines. The first academic paper in this issue, Search Behavior and Opinions of Undergraduate Student Users of a Library Discovery System by Shih-Chuan Chen, explores the usage patterns of university students in library resource discovery. The study employs observational methods and semi-structured in-depth interviews to analyze user behavior. Based on the findings, recommendations are made to optimize interface design and provide usage guidance to enhance the overall user experience. The second academic paper, The Curriculum Design for Scholarly Communication by Yuan-Ho Huang, Tzu-Heng Chiu, and Hsin-Liang Chen, examines the course syllabi of accredited library and information science master’s programs in the U.S. and Canada while incorporating insights from Taiwanese educators through focus group interviews. The study proposes a framework for academic communication courses, covering seven key areas, including academic publishing, open science, intellectual property rights, and the role of librarians. This framework may serve as a reference for course design in Taiwan, aiming to train future librarians and enhance the quality of academic communication services. The third academic paper, Using Semantic Analysis Tools to Explore the Media Framework and Impacts of Archival-Themed News during Taiwan’s Presidential Elections by Chyi-Kuan Wang, applies text mining techniques to analyze archival news reports published during Taiwan’s presidential elections from 2000 to 2020. The study examines 534 reports from United Daily News, Liberty Times, and China Times, analyzing keyword frequencies and thematic clustering. The results show that archival news predominantly focuses on “archive,” “history,” “government,” and “president,” frequently referencing figures such as Chiang Kai-shek, Chiang Ching-kuo, and Lee Teng-hui, with particular emphasis on the 1999-2000 political transition and the “One China” debate. The study provides insights into how media use archives to shape electoral narratives, contributing to the analysis of news framing. The fourth academic paper, A Study of Information-Access and Fake Information Experiences among Elementary School Teachers by An-Huan Guo and Lih-Juan ChanLin, investigates how elementary school teachers in Taiwan obtain information and respond to misinformation. Based on a survey of 199 teachers, the study finds that teachers primarily rely on online resources for general information but exercise greater caution when verifying teaching-related information. About 70% of respondents have used fact-checking tools and acknowledge that misinformation tends to be mistakenly accepted as true over time. The study also highlights the significant emotional impact of misinformation and proposes preventive measures for relevant stakeholders. The work report, Analysis of AI Policy Guidelines at Taiwan’s Top Universities and Teaching Exploration at NCCU Libraries by Chun-Ru Ko, examines the ethical challenges posed by generative AI in higher education, particularly its impact on academic integrity. The analysis of AI policy guidelines from 16 top universities in Taiwan reveals that most policies focus on faculty rather than providing specific guidance for students, with only two universities mentioning the role of libraries. The report shares the experience of NCCU Library in enhancing AI literacy education since September 2023 and outlines its initiatives in 2024. It emphasizes responsible AI usage within academic ethics and explores the evolving role of academic libraries in guiding students toward ethical AI practices. Overall, while this issue covers a wide range of topics, they are united by a common theme: the exploration of information and the transmission of knowledge, which are central to the mission of this journal. We sincerely thank the authors for their contributions and insights and look forward to more scholars and experts collaborating to further develop this field.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.276 | 0.152 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".