Every researcher has biases. Too often I don’t see those backgrounds acknowledged when teaching academic writing, yet it certainly shapes how we teach — and how we learn. I want to mention mine so you know how my thinking shapes this hub.
Knowledge as interpretation
I assume knowledge is our current best interpretation of empirical data, framed as research questions answered with evidence. This interrogative mode—asking “how does the world work?”—shapes how I structure writing advice. This means I assume a “research question → most likely answer” framing when writing for scientific journals, which requires handling uncertainty in Discussion writing not explicitly taught in all disciplines.
Societal/interdisciplinary work
I was trained as an (applied) ecologist. That means I’m used to writing IMRaD papers with a heavy focus on statistical analysis, societal applications and interdisciplinary research, and less used to writing more fundamental research papers or ethical considerations.
Explicit logic + clear language
During my master’s I worked in a research group heavily focused on clear, logical decision making. This influenced my thinking and skewed it further towards preferring explicit logic, and clear language — as opposed to the implicit reasoning and more traditional academic language preferred in other academic sub-cultures.
Perfectionism
Most of the 20 - Process posts are written keeping my own struggles with writing in mind, including perfectionism, not knowing how writing works, procrastination, etc. This means I tend to emphasise low-stakes drafting and ‘permission to be messy’ over grammar perfection.
A note on AI usage
While I have mixed view on the use of AI (lots of environmental guilt), I also love the way it speeds up getting ideas across. It can phrase things in ways I can’t do as quickly myself. That means that certain parts of the hub have been drafted with AI — and then heavily edited by myself to make sure it actually makes sense what it wrote.