Sabina Ratajczak – författare

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

Research Ethics, Integrity, and Transparency guides researchers through ethics, integrity, and transparency when using big data, social media, and AI, with practical advice on consent, GDPR, bias, and open science. Bringing research ethics into the realities of digital data, this volume helps readers plan, run, and publish studies that can stand up to scrutiny. It reframes ethics and transparency as enabling conditions for trustworthy work, not a bureaucratic afterthought, and shows how to translate principles into defensible choices across the full research workflow.Written for PhD candidates and early-career researchers across the social sciences and management, this volume also supports supervisors, methods instructors, research ethics committees, and research offices who need an up-to-date reference for integrity and transparency in the digital age.

AI in Social Sciences Research explains how artificial intelligence (AI) is reshaping social inquiry, from data collection and research design through analysis, interpretation, and theory-building. Treating AI as both a tool kit and a methodological shift, the volume offers a clear route into machine learning (ML), natural language processing (NLP), simulation, and generative AI (GAI) for social researchers while keeping ethics, bias, transparency, and interpretability in view.Designed for postgraduate students, early-career researchers, and instructors teaching digital and computational social science (CSS) methods, the book supports readers who want to use AI with methodological care, not just technical speed.