My experience as a Section Editor for JWRPM
in writing
A brief overview on my experience as a Section Editor for JWRPM
These months are marking the end of my experience as a Section Editor for the Journal of Water Resources Planning and Management. Here’s a list of common issues I found in the papers I handled during the past years:
- A VERY common issue I found were papers with a poor Introduction. What does that mean?
- Weak or confusing description of the macro problem(s) addressed in the study –> Make your problem statement clear, tangible, and accessible to a broad audience.
- Unstructured literature review, namely a long list of previously-published articles that does not identify any specific issue –> Identify the gaps in the existing literature and map them to the problem statement.
- Unclear description of the specific contributions –> Having identified macro problem(s) and gaps in the state of the art, point the reader to your contributions. What is novel? Why does it matter?
- A second issue concerns the experimental setup.
- Unclear descriptions of the data used –> This may sound basic, but make an effort to help the reader understand which data you used, and why.
- Lack of a benchmarking exercise. Many papers introduce an algorithm / model to improve the current methodological approach to a given problem .. shouldn’t we compare it against the existing approaches? –> Consider a benchmarking exercise!
- Unclear description of modelling assumptions –> Don’t be shy here … describe the assumptions and explain why you adopted them. I found that doing this works much better than avoiding to introduce them. Reviewers will catch them ..
- Lack of a proper discussion. Often, discussions presented a summary of methods and results … that’s an extended abstract, not a Discussion. So, what should one write?
- –> Summarise your results, but then explain why they matter … what’s their broader impact? What are the implications of your findings?
- –> Openly discuss your findings in light of modelling assumptions and limitations.
- –> race back to the opening of your paper–to the big problem(s)–and ask yourself how your work helped tackle it … and, finally, create a roadmap for what should be done next.
This last point is not an issue, just a wish: several papers are not making their data, code, and protocols available. What’s the point of proposing a new algorithm if nobody can use it?
I know that the English Language is a great barrier for academic writing; I believe most editors and reviewers understand that. But I also believe that many of the points mentioned above can be addressed without being proficient English writers.
- Many issues I mentioned are broadly discussed in courses and books on Academic Writing. My favourite book is Writing Science by Joshua Schimel. I’m super grateful to Sean Turner who first introduced me to it.
