A plain language summary of my paper on habitual dismissals
Chat GPT vs Me
First, I will post my version of the “Plain Language Summary” from my forthcoming paper on the U.S. Courts’ habitual dismissal culture that is tearing America apart.
Next, I will post what Chat GPT produced as a more concise version of it.
While I admit that Chat GPT writes better than I do in some ways, it has no personality and it is certainly not a reflection of my writing style. So, I am going to stick with my original work though pressure from others compelled me to try this exercise. Of course, Chat GPT would have produced nothing had I not first input my version. AI is all “A” and no “I.”
Mine
Plain Language Summary
The People are held together as a civil society through access to the courts for dispute resolution, effected through the U.S. Constitution Article III Section 2, known as the “cases and controversies” clause.
The Supreme Court of the United States (SCOTUS) functionally manages the lower courts through their decisions in cases selected to be heard by SCOTUS. The District Courts broadly interpret the pleading standards manifest in Twiqbal to be significantly heightened, resulting in dismissals of meritorious claims. The habits formed in dismissing cases based on pleading standards spilled over to dismissing a greater number of cases on standing, summary judgment, qualified immunity, mootness, ripeness, and other dismissal doctrines.
The 62.2% extra caseload in 2020 accelerated the habitual dismissals. The case citation rates of Twiqbal, nearly ten (10) times greater than the seminal case used in standing doctrine, Lujan v. Defenders of Wildlife (1992), are strong evidence that habitual dismissal acceleration was caused by interpretations of the Twiqbal decisions. The data shows that this same pattern of doctrinal dismissal acceleration occurred a couple decades earlier for summary judgment standards based on Anderson v. Liberty Lobby (1986) and Celotex v. Catrett (1986).
In summary, 2020 brought such a stark increase in caseload that it was impossible for the court system to hear all the meritorious cases. For survival, under the overwhelming workload event, courts heightened the pleading threshold to hear a case. Dismissals under Twiqbal became increasingly habitual, and the habit became part of the systemic organizational behavior of the U.S. Court system in 2020 and thereafter. Years 2021 and 2023 also brought significant excess caseload thus solidifying the accelerated systemic and previously formed habit pleading stage dismissal of meritorious cases.
Habit formation modeling is explored in this paper. Some of the models cited herein match the events of 2020 including the overwhelming workload event.
Chat GPT’s
Plain Language Summary - from Chat GPT
Civil society depends on fair access to the courts, guaranteed by the Constitution’s “cases and controversies” clause. Over time, Supreme Court decisions—especially Twombly (2007) and Iqbal (2009)—have led lower courts to demand more detailed pleadings before allowing cases to proceed. This heightened standard has caused many legitimate claims to be dismissed early.
The surge in court filings in 2020, up more than 60% from expected levels, intensified this pattern. Faced with overwhelming caseloads, courts relied increasingly on procedural dismissals as a coping mechanism, turning this practice into an institutional habit. Similar trends appeared decades earlier with summary judgment rules from Anderson and Celotex (1986).
This paper examines how those habits formed and how the courts’ procedural focus, reinforced by heavy workloads, has limited Americans’ ability to have their disputes fully heard and resolved.
God bless you all
If you don’t use it, you lose it!
A very apt expression on why I think AI is dumbing down our society. The grey matter in our heads needs to be exercised to remain active and resilient.
AI may have a place in analyzing complex scientific research but for everyday communication with others, it’s just dumbing us down!
Have you ever thought of running your writing through a readability program?
One on line program found that your sample scored 14.46. This means that only professionals who read at the college level will be able to understand what you wrote. If this is your intended audience, you will be fine.
Chat GPT didn't do much better.
I think if you run most best sellers through a program like this, you will find that the target audience reads at about the 8th grade level.