Research misconduct in hospitals is spreading:A bibliometric analysis of retracted papers from Chinese university-affiliated hospitals

Purpose: The number of retracted papers from Chinese university-affiliated hospitals is increasing, which has raised much concern. The aim of this study is to analyze the retracted papers from university-affiliated hospitals in China’s mainland from 2000 to 2021. Design/methodology/approach: Data for 1,031 retracted papers were identified from the Web of Science Core collection database. The information of the hospitals involved was obtained from their official websites. We analyzed the chronological changes, journal distribution, discipline distribution and retraction reasons for the retracted papers. The grade and geographic locations of the hospitals involved were explored as well.Findings: We found a rapid increase in the number of retracted papers, while the retraction time interval is decreasing. The main reasons for retraction are plagiarism/self-plagiarism(n=255), invalid data/images/conclusions(n=212), fake peer review(n=175) and honesty error(n=163). The disciplines are mainly distributed in oncology(n=320), pharmacology & pharmacy(n=198) and research & experimental medicine(n=166). About 43.8% of the retracted papers were from hospitals affiliated with prestigious universities. Research limitations: This study fails to differentiate between retractions due to honest error and retractions due to research misconduct. We believe that there is a fundamental difference between honest error retractions and misconduct retractions. Another limitation is that authors of the retracted papers have not been analyzed in this study.Practical implications: This study provides a reference for addressing research misconduct in Chinese university-affiliated hospitals. It is our recommendation that universities and hospitals should educate all their staff about the basic norms of research integrity, punish authors of scientific misconduct retracted papers, and reform the unreasonable evaluation system.Originality/value: Based on the analysis of retracted papers, this study further analyzes the characteristics of institutions of retracted papers, which may deepen the research on retracted papers and provide a new perspective to understand the retraction phenomenon.

Who is innocent in authorship misconduct?

Recently, the editorial office received a requisition from one of the co-corresponding authors of an article published in Zoological Research in 2003. This researcher claimed he was not informed that he was listed as an author during the entire manuscript submission and publication process. Moreover, he had a concern about the reliability of the data in the paper. Therefore, he would like to withdraw his authorship of this particular article or withdraw this article entirely. The editorial office forwarded the letter to the other authors to collect comments, and the first author completely denied the co-corresponding author＇s claim of unawareness of authorship by providing archived emails between them. Setting aside what really happened 13 years ago, in this case, it may be interpreted that either this cocorresponding author himself is announcing his honorary authorship （either by passively being added to the byline or actively accepting the offer） in this article, or is trying to avoid taking （potential） responsibility regarding the research content by using honorary authorship as a defense. Meanwhile, the first author has been accused of offering honorary authorship to a senior researcher.

AMDnet:An Academic Misconduct Detection Method for Authors’Behaviors

In recent years,academic misconduct has been frequently exposed by the media,with serious impacts on the academic community.Current research on academic misconduct focuses mainly on detecting plagiarism in article content through the application of character-based and non-text element detection techniques over the entirety of a manuscript.For the most part,these techniques can only detect cases of textual plagiarism,which means that potential culprits can easily avoid discovery through clever editing and alterations of text content.In this paper,we propose an academic misconduct detection method based on scholars’submission behaviors.The model can effectively capture the atypical behavioral approach and operation of the author.As such,it is able to detect various types of misconduct,thereby improving the accuracy of detection when combined with a text content analysis.The model learns by forming a dual network group that processes text features and user behavior features to detect potential academic misconduct.First,the effect of scholars’behavioral features on the model are considered and analyzed.Second,the Synthetic Minority Oversampling Technique(SMOTE)is applied to address the problem of imbalanced samples of positive and negative classes among contributing scholars.Finally,the text features of the papers are combined with the scholars’behavioral data to improve recognition precision.Experimental results on the imbalanced dataset demonstrate that our model has a highly satisfactory performance in terms of accuracy and recall.

Authorship misconduct

DEAR EDITOR： We read the publication by Liu （2016） on authorship misconduct with a great interest. In fact, the authorship misconduct is no uncommon and has to be managed, Liu （2016） noted that ＂Proper authorship embodies honesty, integrity, fairness and transparency, which surely are the very essence of any scientific pursuit.＂ Due to the present requirement on academic publication to support academic rank, several forms of authorship misconduct can be seen. Of interest,

＂Winning Is Everything＂ as the Basis of Academic Misconduct Among Indonesian Students

Unethical behavior among university students such as cheating and plagiarism has weakened the character of honesty in education. This fact has challenged those who perceived education as a holistic process of internalizing values and norms that lead to the formation of students＇ moral principles and moral behaviour. Educators have played the role of ensuring the students to internalize and realized moral values and norms. A study of 360 students of the second semester who enrolled at the course of ＂ethical and personal development＂ at Atma Jaya Catholic University in Indonesia showed that unethical behavior such as cheating and plagiarism were rarely done. However, a deep look at the reason the students did academic dishonesty has prompted the permissiveness of student＇s moral life. This study proves that academic integrity among university students is worrisome, and it is worsened by the fact that they were enrolled in the course of ＂ethical and personal development＂. Seriously taking into consideration the strong desire of students to change the culture of academic misconduct, the authors argue that an educational model which is not oriented excessively to cognitive performance is needed. The authors argue that this position has to be practiced in line with the involvement of ＂clean＂ students who are involved as role models in influencing the formation of student awareness and ethical behavior.

Using a novel ensemble learning framework to detect financial reporting misconduct

Our research focuses on detecting financial reporting misconduct and derives acomprehensive misconduct sample using AAERs and intentional restatements.We develop a novel ensemble learning method, Multi-LightGBM, for highlyimbalanced classification learning. We adopt a human-machine cooperationfeature selection method, which can mitigate the limitation of incompletetheories, enhance the model performance, and guide researchers to develop newtheories. We propose a cost-based measure, expected benefits of classification,to evaluate the economic performance of a model. The out-of-sample testsshow that Multi-LightGBM, coupled with the features we selected, outperformsother predictive models. The finding that introducing intentional materialrestatements into our predictive model does not reduce the effectiveness ofcapturing AAERs has important implications for research on AAERsdetection. Moreover, we can identify more misconduct firms with fewerresources by the misconduct sample relative to the standalone AAERs sample,which is quite beneficial for most model users.

以“智能向善”推进算法推荐的乱象治理

算法是互联网时代的核心技术支撑,但在具体应用情境中出现了信息定向推送造成的“茧房”效应、算法应用平台过度追求效率导致的操控效应、用户路径依赖带来的“杀熟”效应等算法推荐乱象。对算法推荐不良行为的自我辩护,坚持的是一种技术“工具—独立”的价值观,遵循唯技术主义,但脱离了具体的情境应用。推进算法推荐乱象的治理和行为纠偏,应在算法推荐应用情境中构建以“智能向善”为价值导向的治理体系。这需要从源头设计层面赋予算法编码以正向的价值导向,从运行应用层面强化算法应用平台的主体责任建构,从政策保障层面厘清算法应用平台的利益诉求边界,以及从监管实施层面提升监管部门甄别处理流量事件的应急能力。

南稻北麦与公司违规——基于水稻理论的实证研究

利用2003—2018年中国上市公司违规行为的数据,研究了水稻种植与公司违规之间的联系及其内在机制。研究表明,随着CEO籍贯地种植水稻的比例提高,公司违规的概率也相应增大,稻作经济所带来的低社会信任度和风险偏好文化是其传导机制。儒家文化影响力与稻作文化在影响公司违规方面存在一定的替代关系,CEO学历的提高能在一定程度上抑制稻作文化对公司违规的正向影响。基于舞弊三角理论,检验了在不同压力与机会要素下,水稻种植的异质性影响,发现CEO籍贯地水稻种植比例在经营压力小、违规机会大的公司能够对公司违规起到更大影响。

Amend:An integrated platform of retracted papers and concerned papers

Purpose:The notable increase in retraction papers has attracted considerable attention from diverse stakeholders.Various sources are now offering information related to research integrity,including concerns voiced on social media,disclosed lists of paper mills,and retraction notices accessible through journal websites.However,despite the availability of such resources,there remains a lack of a unified platform to consolidate this information,thereby hindering efficient searching and cross-referencing.Thus,it is imperative to develop a comprehensive platform for retracted papers and related concerns.This article aims to introduce“Amend,”a platform designed to integrate information on research integrity from diverse sources.Design/methodology/approach:The Amend platform consolidates concerns and lists of problematic articles sourced from social media platforms(e.g.,PubPeer,For Better Science),retraction notices from journal websites,and citation databases(e.g.,Web of Science,CrossRef).Moreover,Amend includes investigation and punishment announcements released by administrative agencies(e.g.,NSFC,MOE,MOST,CAS).Each related paper is marked and can be traced back to its information source via a provided link.Furthermore,the Amend database incorporates various attributes of retracted articles,including citation topics,funding details,open access status,and more.The reasons for retraction are identified and classified as either academic misconduct or honest errors,with detailed subcategories provided for further clarity.Findings:Within the Amend platform,a total of 32,515 retracted papers indexed in SCI,SSCI,and ESCI between 1980 and 2023 were identified.Of these,26,620(81.87%)were associated with academic misconduct.The retraction rate stands at 6.64 per 10,000 articles.Notably,the retraction rate for non-gold open access articles significantly differs from that for gold open access articles,with this disparity progressively widening over the years.Furthermore,the reasons for retractions have shifted from traditional individual behaviors like falsification,fabrication,plagiarism,and duplication to more organized large-scale fraudulent practices,including Paper Mills,Fake Peer-review,and Artificial Intelligence Generated Content(AIGC).Research limitations:The Amend platform may not fully capture all retracted and concerning papers,thereby impacting its comprehensiveness.Additionally,inaccuracies in retraction notices may lead to errors in tagged reasons.Practical implications:Amend provides an integrated platform for stakeholders to enhance monitoring,analysis,and research on academic misconduct issues.Ultimately,the Amend database can contribute to upholding scientific integrity.Originality/value:This study introduces a globally integrated platform for retracted and concerning papers,along with a preliminary analysis of the evolutionary trends in retracted papers.

网络主播失范行为及伦理引导

随着4G网络的普及,网络直播日益成为人们表达情感、满足需要、开展娱乐的重要手段。在政府、媒体、大众普遍看好网络直播行业发展前景的同时,各种违规、违法、失范、失德现象也日益突出,有的甚至已经构成严重犯罪。国家虽然颁布了一系列法律法规,但是网络失范行为仍旧时有发生。常见的网络失范行为有五种形式,包括网络欺诈、网络色情、网络恶搞、网络暴力、网络侵权,它们不但污染了网络生态环境,而且有悖社会主义核心价值观及公序良俗。导致网络主播持续失范的原因很多,从“现代性的隐忧”来看,主要包括市场机制被滥用,道德情感被割裂,社会责任被破坏,职业精神被亵渎四个方面。对于一般性失范行为而言,加强道德教育与伦理引导至关重要,其可以弥补“他律”的不足,提高“自律”的效能以及降低执法的成本。从康德的“定言命令”入手,通过弘扬当代美德伦理、重构常识道德体系、践行网络社交礼仪、谨防非正常快乐观,构建传统与现代,线上与线下相统一的伦理规范,是缓解“大脱嵌”带来的痛苦,实现个人自由以及网络生态和谐的必由之路。

我国研究生教育学术不端治理的发展态势、困境与对策探析

学术创新在建制化规范化正向化发展过程中需要抵制和有效治理各种学术不端行为,这也是培养和提高研究生学术诚信意识的重要内容。随着理论和实践的深入发展,学术不端治理越来越成为一个独立的研究议题和科技治理的重要环节,也是促使研究生提高科研诚信意识和科研伦理主动性以及有效规避学术不端的重要推手。总结我国学术不端治理理论和实践发展经验,是直面新时代学术不端治理困境、推进问题解决的前提。从我国学术不端治理实践呈现的体系化、联动性、主动性、全面化良好态势出发,更要认识到我国学术不端治理在认知基础、价值辩护、局面被动以及量化评价等方面面临的普遍困境,通过构建学术不端治理行动者网络、加强学术诚信教育、科学借鉴科技发达国家成熟经验等提升学术不端治理成效。

内容电商营销行为失范及规范思考

内容电商以“内容+电商”的商业化呈现,将各类文化知识进行数字转化,以内容创作、数字传播引发购物兴趣,借助社交优势将内容创业中的内容进行文化传播、内容变现。但在内容创意生成、内容数据利用、内容文化传播、平台商事运作等内容电商营销行为中,也夹杂着一些不规范现象。有必要针对内容创意、内容传播、电商运营行为的不合规处,进行相应的行为规范、行为引导,以优质的内容创意保障、正当的内容营销数据,防控内容电商营销风险。

发挥科技期刊在科研诚信建设中的作用——对学术不端及其边缘行为作者的教育

为了进一步发挥科技期刊在科研诚信建设中的作用,提出对学术不端及其边缘行为作者的教育。通过阐述学术不端及其边缘行为的主要原因,分析科技期刊教育的作用及优势,进而提出对学术不端作者及其边缘行为作者教育的具体措施。

新媒体背景下科技期刊编辑如何防范学术不端行为

防范学术不端行为是科技期刊编辑面临的一项艰巨而复杂的任务,通过分析新媒体背景下学术不端行为的产生原因,并对其具体表现形式进行归纳分析,提出了完善制度和规范处理流程、采用多种技术手段、提升科技期刊编辑自身素养和能力等防范学术不端行为的措施,希望能为科技期刊的健康发展保驾护航。

高校附属医院科研人员学术不端认知现状调查

目的:了解高校附属医院科研人员对学术不端的认知和态度,探讨引发学术不端行为的影响因素,以提出防范学术不端行为的有效措施。方法:采用线上调查,对来自全国29所高校附属医院的531名科研人员进行问卷调查,问卷内容包括科研人员的基本信息、对科研诚信制度的了解程度,对学术不端行为的认知、态度及其影响因素。结果:高校附属医院科研人员对科研诚信各项制度的知晓率在66.10%~78.53%;受访人员反对学术不端行为的比例为32.96%~78.34%。学术不端行为产生的主要原因有职称晋升、评奖评优的驱动(87.57%)、临床工作压力(83.80%)、医疗机构的科研环境(83.24%)等。结论:为防范高校附属医院科研人员学术不端行为,亟需强化科研诚信教育,完善学术成果评价和人才评聘体系,减轻科研人员工作压力,健全学术不端行为监管体系。

从权力规制走向关系治理:基于社会网络分析的高校内部权力失范及其治理研究

高校内部权力的规范、有序、高效运行是高校权力规制的价值追求。党的十八大以来,高校惩治和预防腐败体系建设成效显著,权力腐败现象得到有效遏制,但影响相对较小、隐匿性强的权力失范现象仍时有发生。对高校内部权力失范案例进行梳理发现,“关系”是隐匿在权力失范背后的力量。从社会网络分析的视角看,高校内部权力主体的社会关系网络具有富集性、自主性和联通性等特征。高校存在“首长”主导型、跨部门“合谋”型、同部门“合谋”型三类关系结构截然不同的小团体。高校应从重视权力规制走向重视关系治理,从重视制度建设走向重视规则完善,从重视决策过程走向重视决策监管,将权力运行场域重塑、权力行为规范和权责意识强化有机统一起来,有效实现对权力失范的系统治理。

医学院校在校研究生学术不端认知情况调查研究

目的 了解普通高等医学院校在校研究生学术不端认知和行为选择现状,完善学术道德的教育。方法于2022年1月4日通过电子调查问卷,以专业年级为单位,对右江民族医学院2019—2021级在校研究生进行学术不端的认知和行为调查。结果 通过对198份有效问卷进行分析,发现有135名(68.2%)研究生表示对学术不端一般了解;有122名(61.6%)研究生表示非常重视平时学术论文写作过程中学术诚信的相关规范;有163名(82.3%)研究生认为坚决抵制,严厉惩罚论文抄袭行为。利益驱使、自身学术道德和法律意识差、有论文发表数量规定是学术不端、学术论文不诚信现象的主要原因。结论 医学研究生对学术不端的认知度有所欠缺,学校要通过加强研究生的主观教育,提升自律意识,建立健全的相关规范制度、落实导师督导责任制,强化他律的机制建设等措施预防学术不端行为的发生。

2013—2023年出版学术不端行为文献计量学分析

基于CNKI数据源,筛选了2013—2023年所收录的学术不端行为相关516篇文献作为主要研究对象。利用文献计量学方法,对学术不端行为的文献研究总体现状、发文量、发文区域、研究热点、基金资助情况、高被引论文、高下载论文及其发展脉络进行了系统梳理与分析。结果发现,出版学术不端行为研究受到学者们的高度重视,相关文献量呈上升趋势。通过对近10年学术不端领域文献的研究,提出防范学术不端的措施,建立行之有效的管理制度,促进学术界的共同进步和发展。

医学论文学术不端行为及防范对策探究

通过了解医学论文学术不端行为的定义、分类,分析医学论文学术不端产生的主要原因可能与学术评价体系以及对学术不端行为缺乏监管与惩戒等原因有关。借鉴国际经验,结合本国的国情及法律政策,探讨防范对策,加强学术道德教育;推广数据共享,提高学术透明度;加强学术审查和监管;引入学术行为办公系统,建立学术失信学者库;加快学术评价体系的改革等,防范学术不端行为的产生,推动医学进步,促进学科发展。

大数据赋能研究生学术不端行为治理——价值意蕴、现实梗阻与推进策略

作为一种全新的思维方式和先进的技术手段,大数据赋能研究生学术不端行为治理的价值意蕴主要体现在学术成果不断数据化让抄袭造假行为难以藏身、大数据检测软件防控各类学术不端行为的发生、互联网黑名单系统完善学术不端行为惩戒机制、聚类分析有助于开展针对性强的学术道德教育。然而,大数据赋能研究生学术不端行为治理也面临着学术监督主体缺乏使用大数据的行为意向、各类科研数据库的开放共享程度亟待提高、科研成果价值开发和合理使用的矛盾加剧、大数据固有缺陷妨碍学术不端行为的治理等现实梗阻。为此,需要打通科研数据开放共享的融合渠道、研发基于大数据的学术不端智能监督系统、提高学术不端监督主体的数据素养;全方位撑起科研数据的安全保护伞。