Thanks a lot for your comment. The studies that look at Judicial delay suffer from a significant limitation—most of them are small-n studies relying on manual reading of a bunch of documents and classifying them under different possible causes. In my undergrad thesis, I am exploring ways to automate some of this through NLP, and will hopefully have more to say on this in future.
I am skeptical of sighting some large-n studies because they seem to have misleading results. The one I cite above—VIDHI’s Delhi High Court study—while the best IMO, is also geographically constrained. This largely happens because we do not have the first step—datasets that reliably code the causes of judicial delay for large number of cases from a swathe of courts.
Thanks a lot for your comment. The studies that look at Judicial delay suffer from a significant limitation—most of them are small-n studies relying on manual reading of a bunch of documents and classifying them under different possible causes. In my undergrad thesis, I am exploring ways to automate some of this through NLP, and will hopefully have more to say on this in future.
I am skeptical of sighting some large-n studies because they seem to have misleading results. The one I cite above—VIDHI’s Delhi High Court study—while the best IMO, is also geographically constrained. This largely happens because we do not have the first step—datasets that reliably code the causes of judicial delay for large number of cases from a swathe of courts.