arXiv subjects

Puyu Yang

Publications and source records attributed to Puyu Yang.

7 recordsLinked to original sources

arXiv2025

Contested Citations: The Role of Open Access Publications in Wikipedia's Scientific Disputes

Wikipedia is one of the largest online encyclopedias, which relies on scientific publications as authoritative sources. The increasing prevalence of open access (OA) publishing has expanded the public availability of scientific knowledge; however, its impact on the dynamics of knowledge contestation within collaborative environments such as Wikipedia remains underexplored. To address this gap, we analyze a large-scale dataset that combines Wikipedia edit histories with metadata from scientific publications cited in disputed Wikipedia articles. Our study investigates the characteristics of scientific publications involved in disputes and examines whether OA articles are more likely to be contested than paywalled ones. We find that scientific disputes on Wikipedia are more frequent in the social sciences and humanities, where topics often involve social values and interpretative variability. Publications with higher citation counts and publications in high-impact journals are more likely to be involved in disputes. OA publications are significantly more likely to be involved in disputes and tend to be contested sooner after publication than paywalled articles. This pattern suggests that increased accessibility accelerates both engagement and scrutiny. The relationship between OA status and dispute involvement also varies across disciplines, reflecting differences in Wikipedia editorial practices and norms. These findings highlight the dual role of OA in both expanding access to scientific knowledge and increasing its visibility in contexts of public negotiation and debate. This study contributes to a broader understanding of how scientific knowledge is collaboratively constructed and contested on open platforms, offering insights for research on open science, scholarly communication, and digital knowledge governance.

cs.DL↗

arXiv2025

PHM-Bench: A Domain-Specific Benchmarking Framework for Systematic Evaluation of Large Models in Prognostics and Health Management

With the rapid advancement of generative artificial intelligence, large language models (LLMs) are increasingly adopted in industrial domains, offering new opportunities for Prognostics and Health Management (PHM). These models help address challenges such as high development costs, long deployment cycles, and limited generalizability. However, despite the growing synergy between PHM and LLMs, existing evaluation methodologies often fall short in structural completeness, dimensional comprehensiveness, and evaluation granularity. This hampers the in-depth integration of LLMs into the PHM domain. To address these limitations, this study proposes PHM-Bench, a novel three-dimensional evaluation framework for PHM-oriented large models. Grounded in the triadic structure of fundamental capability, core task, and entire lifecycle, PHM-Bench is tailored to the unique demands of PHM system engineering. It defines multi-level evaluation metrics spanning knowledge comprehension, algorithmic generation, and task optimization. These metrics align with typical PHM tasks, including condition monitoring, fault diagnosis, RUL prediction, and maintenance decision-making. Utilizing both curated case sets and publicly available industrial datasets, our study enables multi-dimensional evaluation of general-purpose and domain-specific models across diverse PHM tasks. PHM-Bench establishes a methodological foundation for large-scale assessment of LLMs in PHM and offers a critical benchmark to guide the transition from general-purpose to PHM-specialized models.

cs.AI↗

arXiv2025

UBMF: Uncertainty-Aware Bayesian Meta-Learning Framework for Fault Diagnosis with Imbalanced Industrial Data

Fault diagnosis of mechanical equipment involves data collection, feature extraction, and pattern recognition but is often hindered by the imbalanced nature of industrial data, introducing significant uncertainty and reducing diagnostic reliability. To address these challenges, this study proposes the Uncertainty-Aware Bayesian Meta-Learning Framework (UBMF), which integrates four key modules: data perturbation injection for enhancing feature robustness, cross-task self-supervised feature extraction for improving transferability, uncertainty-based sample filtering for robust out-of-domain generalization, and Bayesian meta-knowledge integration for fine-grained classification. Experimental results on ten open-source datasets under various imbalanced conditions, including cross-task, small-sample, and unseen-sample scenarios, demonstrate the superiority of UBMF, achieving an average improvement of 42.22% across ten Any-way 1-5-shot diagnostic tasks. This integrated framework effectively enhances diagnostic accuracy, generalization, and adaptability, providing a reliable solution for complex industrial fault diagnosis.

cs.LG↗

arXiv2025

Research Data in Scientific Publications: A Cross-Field Analysis

Data sharing is fundamental to scientific progress, enhancing transparency, reproducibility, and innovation across disciplines. Despite its growing significance, the variability of data-sharing practices across research fields remains insufficiently understood, limiting the development of effective policies and infrastructure. This study investigates the evolving landscape of data-sharing practices, specifically focusing on the intentions behind data release, reuse, and referencing. Leveraging the PubMed open dataset, we developed a model to identify mentions of datasets in the full-text of publications. Our analysis reveals that data release is the most prevalent sharing mode, particularly in fields such as Commerce, Management, and the Creative Arts. In contrast, STEM fields, especially the Biological and Agricultural Sciences, show significantly higher rates of data reuse. However, the humanities and social sciences are slower to adopt these practices. Notably, dataset referencing remains low across most disciplines, suggesting that datasets are not yet fully recognized as research outputs. A temporal analysis highlights an acceleration in data releases after 2012, yet obstacles such as data discoverability and compatibility for reuse persist. Our findings can inform institutional and policy-level efforts to improve data-sharing practices, enhance dataset accessibility, and promote broader adoption of open science principles across research domains.

cs.DL↗

arXiv2023

Open Access Improves the Dissemination of Science: Insights from Wikipedia

Wikipedia is a well-known platform for disseminating knowledge, and scientific sources, such as journal articles, play a critical role in supporting its mission. The open access movement aims to make scientific knowledge openly available, and we might intuitively expect open access to help further Wikipedia's mission. However, the extent of this relationship remains largely unknown. To fill this gap, we analyze a large dataset of citations from the English Wikipedia and model the role of open access in Wikipedia's citation patterns. We find that both the accessibility (open access status) and academic impact (citation count) significantly increase the probability of an article being cited on Wikipedia. Specifically, open-access articles are extensively and increasingly more cited in Wikipedia, as they show an approximately 64.7% higher likelihood of being cited in Wikipedia when compared to closed-access articles, after controlling for confounding factors. This open-access citation effect is particularly strong for articles with high citation counts and published in recent years. Our findings highlight the pivotal role of open access in facilitating the dissemination of scientific knowledge, thereby increasing the likelihood of open-access articles reaching a more diverse audience through platforms such as Wikipedia. Simultaneously, open-access articles contribute to the reliability of Wikipedia as a source by affording editors timely access to novel results.

cs.DL↗

arXiv2022

Polarization and reliability of news sources in Wikipedia

Wikipedia is the largest online encyclopedia: its open contribution policy allows everyone to edit and share their knowledge. A challenge of radical openness is that it facilitates introducing biased contents or perspectives in Wikipedia. Wikipedia relies on numerous external sources such as journal articles, books, news media, and more. News media sources, in particular, take up nearly third of all citations from Wikipedia. However, despite their importance for providing up-to-date and factual contents, there is still a limited understanding on which news media sources are cited from Wikipedia. Relying on a large-scale open dataset of nearly 30M citations from English Wikipedia, we find a moderate yet systematic liberal polarization in the selection of news media sources. We also show that this effect is not mitigated by controlling for news media factual reliability. Our results contribute to Wikipedia's knowledge integrity agenda in suggesting that a systematic effort would help to better map potential biases in Wikipedia and find means to strengthen its neutral point of view policy.

cs.DL↗

arXiv2022

A Map of Science in Wikipedia

In recent decades, the rapid growth of Internet adoption is offering opportunities for convenient and inexpensive access to scientific information. Wikipedia, one of the largest encyclopedias worldwide, has become a reference in this respect, and has attracted widespread attention from scholars. However, a clear understanding of the scientific sources underpinning Wikipedia's contents remains elusive. In this work, we rely on an open dataset of citations from Wikipedia to map the relationship between Wikipedia articles and scientific journal articles. We find that most journal articles cited from Wikipedia belong to STEM fields, in particular biology and medicine ($47.6$\% of citations; $46.1$\% of cited articles). Furthermore, Wikipedia's biographies play an important role in connecting STEM fields with the humanities, especially history. These results contribute to our understanding of Wikipedia's reliance on scientific sources, and its role as knowledge broker to the public.

cs.DL↗