Research Method Usage across Academic Ages in Library and Information Science: An Empirical Study (1990-2023)
- URL: http://arxiv.org/abs/2606.21862v1
- Date: Sat, 20 Jun 2026 03:57:28 GMT
- Title: Research Method Usage across Academic Ages in Library and Information Science: An Empirical Study (1990-2023)
- Abstract summary: Academic age shapes career development, influencing research behavior, output volume, and methodological choices.<n>A corpus of 26,677 articles published 1990-2023 in 14 authoritative Library and Information Science journals was compiled.<n>Mid-career scholars exhibit the highest method diversity; late-career scholars the lowest.
- Score: 7.4287545941586615
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Academic age critically shapes career development, influencing research behavior, output volume, and methodological choices. Analyzing method variation across academic ages offers a new theoretical lens on scholarly evolution and provides early-career researchers with practical guidance for method selection. A corpus of 26,677 articles published 1990-2023 in 14 authoritative Library and Information Science journals was compiled. The CogFT model automatically classified the research methods embedded in these articles, and Top2Vec generated the topic model. This process resulted in a comprehensive dataset linking research methods with topics. Author-name disambiguation enabled calculation of each scholar's academic age. Popularity and Shannon diversity indices for methods, together with topic diversity, were compared across academic age groups. Results reveal dynamic methodological trends: the share of theoretical approaches declined gradually, whereas experimental and bibliometric methods gained ground. Method popularity differs significantly among cohorts. Mid-career scholars exhibit the highest method diversity; late-career scholars the lowest.
Related papers
- (Whose defaults?) Is artificial intelligence reorienting archaeological methods? [0.0]
Generative AI and "vibe coding" are changing how archaeologists carry out computational research.<n>We evaluate whether large language models (LLMs) are narrowing the variety of methods archaeologists use.
arXiv Detail & Related papers (2026-09-10T08:08:15Z) - Gender Differences in Research Topic and Method Selection in Library and Information Science: Perspectives from Three Top Journals [8.370467804153506]
The study offers insights into the specific research design processes that contribute to gender differences in method selection.<n>The findings showed that women tend to use Interview while men prefer Theoretical approach, across a range of topics.
arXiv Detail & Related papers (2026-07-02T07:48:33Z) - Data-Driven Evolution of Library and Information Science Research Methods (1990-2022): A Perspective Based on Fine-grained Method Entities [7.4287545941586615]
Since the 1990s, advancements in big data and information technology have increasingly driven data-centric research in the field of Library and Information Science.<n>This study conducts a fine-grained analysis to uncover the evolutionary trends of research methods within the domain.
arXiv Detail & Related papers (2026-06-24T02:37:13Z) - Gender Differences in Research Topic and Method Convergence among Collaborating Scholars in Library and Information Science [3.5111161561666084]
This study uses a relatively systematic methodology to address the difficulty of studying gender differences in academic publishing.<n>It provides insights into the convergence and diversity of research topics and methods chosen by scholars.
arXiv Detail & Related papers (2026-06-20T07:06:18Z) - Evolution of Research Method Usage Across the Academic Careers of Library and Information Science Scholars [3.2586114800974957]
This study employs a comprehensive dataset comprising full-text journal articles and cognitive records from the Library and Information Science (LIS) domain.<n>This study focuses on 435 senior scholars with an academic age of more than 14 years and a consistent publication record at five-year intervals, covering a total of 6,116 articles.<n>The findings indicate that bibliometric methods are the most frequently used across career stages, accounting for 19.61% among early-career scholars and 31.81% among senior scholars.
arXiv Detail & Related papers (2026-04-22T13:06:58Z) - Large-Scale Multidimensional Knowledge Profiling of Scientific Literature [46.15403461273178]
We compile a unified corpus of more than 100,000 papers from 22 major conferences between 2020 and 2025.<n>Our analysis highlights several notable shifts, including the growth of safety, multimodal reasoning, and agent-oriented studies.<n>These findings provide an evidence-based view of how AI research is evolving and offer a resource for understanding broader trends and identifying emerging directions.
arXiv Detail & Related papers (2026-01-21T16:47:05Z) - Best Practices For Empirical Meta-Algorithmic Research: Guidelines from the COSEAL Research Network [46.56867772369597]
Best practices for meta-algorithmic research exist, but they are scattered between different publications and fields.<n>This report collects good practices for empirical meta-algorithmic research across the subfields of the COSEAL community.<n>It establishes the current state-of-the-art practices within meta-algorithmic research and serves as a guideline to both new researchers and practitioners in meta-algorithmic fields.
arXiv Detail & Related papers (2025-12-18T12:59:45Z) - Transforming Science with Large Language Models: A Survey on AI-assisted Scientific Discovery, Experimentation, Content Generation, and Evaluation [58.064940977804596]
A plethora of new AI models and tools has been proposed, promising to empower researchers and academics worldwide to conduct their research more effectively and efficiently.<n>Ethical concerns regarding shortcomings of these tools and potential for misuse take a particularly prominent place in our discussion.
arXiv Detail & Related papers (2025-02-07T18:26:45Z) - An Instance-based Plus Ensemble Learning Method for Classification of Scientific Papers [2.0794749869068005]
This paper introduces a novel approach that combines instance-based learning and ensemble learning techniques for classifying scientific papers.
Experiments show that the proposed classification method is effective and efficient in categorizing papers into various research areas.
arXiv Detail & Related papers (2024-09-21T19:42:15Z) - ResearchAgent: Iterative Research Idea Generation over Scientific Literature with Large Language Models [56.08917291606421]
ResearchAgent is an AI-based system for ideation and operationalization of novel work.<n>ResearchAgent automatically defines novel problems, proposes methods and designs experiments, while iteratively refining them.<n>We experimentally validate our ResearchAgent on scientific publications across multiple disciplines.
arXiv Detail & Related papers (2024-04-11T13:36:29Z) - A Survey on Deep Semi-supervised Learning [51.26862262550445]
We first present a taxonomy for deep semi-supervised learning that categorizes existing methods.
We then offer a detailed comparison of these methods in terms of the type of losses, contributions, and architecture differences.
arXiv Detail & Related papers (2021-02-28T16:22:58Z) - Topic Space Trajectories: A case study on machine learning literature [0.0]
We present topic space trajectories, a structure that allows for the comprehensible tracking of research topics.
We show the applicability of our approach on a publication corpus spanning 50 years of machine learning research from 32 publication venues.
Our novel analysis method may be employed for paper classification, for the prediction of future research topics, and for the recommendation of fitting conferences and journals for submitting unpublished work.
arXiv Detail & Related papers (2020-10-23T10:53:42Z) - A Survey on Text Classification: From Shallow to Deep Learning [83.47804123133719]
The last decade has seen a surge of research in this area due to the unprecedented success of deep learning.
This paper fills the gap by reviewing the state-of-the-art approaches from 1961 to 2021.
We create a taxonomy for text classification according to the text involved and the models used for feature extraction and classification.
arXiv Detail & Related papers (2020-08-02T00:09:03Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.