Writing Resources > Academics (Page 1 of 8)

Clearly Presenting Interview Quote Data

This article covers the best practices for presenting interview quote data in qualitative research articles and explain how working with a human editor can make the process easier and the final product more effective.

Which Academic Editing Service Do You Need?

Choose the best Scribendi academic editing service for your document. Learn about our editing, proofreading, and review service options for journal articles, theses, dissertations, abstracts, proposals, and other forms of academic and research writing.

Which Editing Service Do You Need?

Find the perfect Scribendi service for your document. Discover our editing, proofreading, and publication options for students, authors, and professionals.

Navigating the Academic Minefield: Understanding and Avoiding Predatory Journals

In the competitive world of academic publishing, researchers are under increasing pressure to "publish or perish," with funding, promotions, and professional recognition often tied to publication records. This intense demand has given rise to an unfortunate phenomenon: predatory journals.

In-text citations: Deceptively difficult for large-language models?

The smartest bots still can’t handle citations. Why human editors remain your best bet.

How AI Can Jeopardize Your Career and How to Avoid It

The pressure to publish can be severe. Academic institutions are committed to the race to report the next big discovery, and it is researchers that must shoulder the burden and deliver. Oftentimes, promotions and career advancements are at stake, hinging on that next paper. Depending on them as a single, quick-fix solution without the insight and expertise of a real, human colleague could result in an embarrassing episode—one that could put your reputation and career at risk.

Can AI Use Be Detected in Academic Writing? Here's What You Need to Know

AI detection tools are increasingly used to review academic writing, but are they accurate? This article explores the flaws in AI detectors—including false positives, lack of transparency, and outdated training—and offers practical steps for students and researchers to validate their work.