应对科研欺诈：机器人、偏见与伪科学.pptx

1、,Navigating Research Fraud:Bots,Bias,&Bad Science,Elaine LasdaCoordinator of Strategic InitiativesUniversity at Albany,SUNYMarch 17,2026Computers in Libraries 2026,Hi Im Elaine!,Bibliometrics Nerd since library schoolAuthor of Metrics Mashup 25 years UAlbanyHome:Catskill,NYCat personHate-Reading ori

2、ginal Oz series,Roadmap:where are we going?,DefinitionsFraud typologyRed FlagsStrategiesResources,A few clarifications,What do we mean by fraud exactly?,Researcher Misconduct compromising integrity within their own work through data manipulation or plagiarism.Systemic or Organized Fraudcoordinated e

3、xternal activities:paper mills exploiting publishing systems,bot-generated publications,etc.,What does“Bots,Bias,and Bad Science”mean?,Automation and BotsManipulate online ecosystems by inflating attention and fabricating engagement to amplify low-quality research.Hoovered into LLMs!Systemic BiasPub

4、lishing incentives and algorithms privilege easy-to-publish work,causing disciplinary,language,and visibility biases.Spectrum of Bad ScienceBad science ranges from honest errors to intentional fraud,complicating evaluation through misleading surface cues.,Why is this important?,Growing Visibility of

5、 Research FraudReports of research fraud are increasing,and media coverage raises public awareness,causing user skepticism and anxiety.Librarys Role in Content DiscoveryLibraries surface research through discovery services and guides,which can unintentionally amplify questionable content if quality

6、signals weaken.Challenges for LibrariansLibrarians must help users evaluate trustworthiness when traditional indicators like peer review and citations become unreliable.,Researcher Initiated,Fabrication and FalsificationMaking up or manipulating data causes research records to misrepresent actual re