A brief flirtation with viral success brought new attention to one of my #Python libraries and some real-world applications of the workings of #OpenSource.

Readers who have stuck around might recall my big project last year. It was a study on the readability of legislation in Singapore and how much “Plain Laws” drafting affected it. (Spoiler alert: limited, if any).

I wrote a Python library called “#redlines” while writing that post. I needed to represent changes in text like the “track changes” function in Microsoft Word, which was the most familiar method to my audience of lawyers. I couldn't find any libraries to do this in Markdown, so I created one and published it anyway.

You can read more on “how it works” in the original post on this library.

I publish most of my coding publicly on my GitHub. I do it with little expectation that anyone would use it. This post discusses my motivations for publicising almost all of my coding work.

So, something strange happened while I was suffering from an acute attack of imposter syndrome last week. My humble Python library suddenly got stars, and I even received a pull request.

Way back in December 2021, I caught wind of the 2020 Revised Edition of the statutes in Singapore law:

Keeping Singapore laws accessible to all - AGC, together with the Law Revision Committee, has completed a universal revision of Singapore’s Acts of Parliament! pic.twitter.com/76TnrNCMUq

— Attorney-General's Chambers Singapore (@agcsingapore) December 21, 2021

The AGC highlighted that the revised legislation now uses “simpler language”. I was curious about this claim and looked over their list of changes. I was not very impressed with them.

However, I did not want to rely only on my subjective intuition to make that conclusion. I wanted to test it using data science. This meant I had to compare text, calculate the changes' readability statistics, and see what changed.