"Parameter" is one of those commonly used words in mathematics and computing and in my experience is rarely explicitly defined. While most uses have similar meanings, there can be small differences in their interpretation. Parameters and other statistical entities are important to the semantics and correctness of the Helical system, so it's worth considering what we mean by these terms.
In my previous post, I asserted:
...learning a new formal language can itself contribute to the difficulty of encoding an experiment.
This statement was based on assumptions, intuitions, and folk wisdom. I started digging into the DSL usability research to see if I could find explicit support for this statement. This blog post is about what I found.
One of the broader goals of the Helical project is to make writing, maintaining, and debugging experiments easier and safer for the end-user through a novel domain-specific language. However, learning a new formal language can itself contribute to the difficulty of encoding an experiment. Therefore, we are intersted in mitigating the effects of language learning/novelty. To this end, a Northeastern coop student (Kevin G. Yang) investigated the suitability of using Jupyter notebooks as an execution environment for experiments last year.
I want to extend a belated welcome to Zixuan (Jason) Yu, a Northeastern University undergraduate student who is working with me on a research coop through December 2025. Jason's project focuses on identifying elements of the Mastodon code base where we might either want to intervene (in order to answer a research question) or where there might be associated privacy considerations.
In 2020 or so, ACM swapped its definitions of reproducibility and replicability to be more in line with the broader social sciences community. I see the reasoning for the swap, but I don't think that the new definition is quite right either. The crux of the issue is that definitions both communities are using are a shorthand that doesn't map appropriately across communities.