What are the burdens researchers face? And what can be done to lighten the load and make the academic environment more diverse, equitable, inclusive, safe, and welcoming?
Policies that formally give peer reviewers the option to officially invite a colleague to collaborate with them improve integrity, transparency, and offers a chance to give fair credit where it is due.
An update on how generative AI has progressed and how it has been applied to research publishing processes since ChatGPT was released, looking at business, application, technology, and ethical aspects of generative AI.
The current uproar over artificial intelligence does not show us what the future of AI will look like, but rather how a human population falls into predictable patterns as it contemplates any new development: we are observing not AI but ourselves observing AI.
Haseeb Irfanullah discusses how Communities of Practice can improve scholarly communications by capitalizing on our collective experiences.
The Supreme Court has ruled in the Andy Warhol–Prince fair use case. What does this mean for scholarly communications, and the reuse of materials for AI training?
Raymond Pun, Sai Deng, and Guoying (Grace) Liu on the challenge of advocating for diversity, equity and inclusion within scholarly communications when your own institution isn’t “there” yet.
Data quality and record keeping are going to grow in importance as a result of AI applications.
The Data Hazards project looks at the problems in applying traditional ethical values to research that uses machine learning and artificial intelligence.
What does the decline of the English major mean for society at large, and university presses in particular?
Christos Petrou takes a look at the Guest Editor model for publishing and its recent impact on Hindawi and MDPI, as Clarivate has delisted some of their journals.
Craig Griffin looks at potential applications we might see for tools like ChatGPT in scholarly publishing. Also included — a research results haiku.
Five pending cases may set new ground rules for use of training materials for AI. Here is what to watch.
Are we still doing the work it takes to make positive and impactful change? Are we continuing the work to break down systems, policies, and unwritten industry rules that are no longer fit for purpose?
Danny Kingsley suggests that research integrity begins with the training researchers receive at university. Achieving Open Research and increasing reproducibility requires systematic research training that focuses specifically on research practice.