Building up your writing productivity

Published 21 September 08 07:40 PM | robyn.owens 

I've been thinking a lot recently about the way in which scholars become productive writers. It's not just that a scholar writes - a productive scholar writes a lot. So, with the analogy of pumping weights at the gym, how can you build up your writing productivity so that the words just flow, and eventually you can pump out a few thousand words a day, and more importantly, a competitive number of journal articles a year?

There are quite a lot of tips on the web for building up your writing productivity. 5 Steps to Boost Writing Productivity suggests: 

  1. Turn off your email
  2. Limit news reading
  3. Just say no to IM
  4. Limit blogging, and
  5. Separate writing from research

Just Write suggests just that:

  1. You should just write 30 - 45 minutes every day, about anything. Just practice the physicality of the activity.
  2. Talk about your story
  3. Write the summary

Another idea is to Have a Baby - read this, it's a good idea about creating deadlines in your writing zone.

I thought that we might be able to share tips on building up writing productivity, with the aim of creating our Top Ten Tips for Boosting Your Research Productivity. If we get more than 10 ideas we'll run a vote to select the top ten.

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# sky said on September 22, 2008 9:53 AM:

Thanks! Those are good ideas, and as I'm (hopefully) in the last few months of my thesis, I need all the help I can get.

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About robyn.owens

I started my academic life doing a BSc (Hons) in Mathematics at UWA before going to Oxford to complete an MSc and a DPhil, also in Mathematics. I then spent three years in Paris at l'Université de Paris-Sud, Orsay, continuing research in mathematical analysis and going to lots of movies before returning to UWA to work as a research mathematician. I have lectured in Maths and Computer Science at UWA, as well as for short periods at Berkeley, The University of Canterbury in Christchurch, and Prince Songkla University in Thailand. My research has focussed on computer vision, including feature detection in images, 3D shape measurement, image understanding, and representation.