Low-Income Litigants in the Sandbox: Court Record Data and the Legal Technology A2J Market
Bibliographic record
Abstract
(Excerpt) Katrina was a community college student with two children, trying to juggle work, childcare, and school. During class in the spring of 2018, her phone buzzed incessantly. She looked down to see a message from her roommate saying a process server had shown up at the house to deliver a summons and complaint, naming Katrina in a lawsuit filed in county court by a debt collection company she had never heard of. Katrina turned to the internet for help and found herself overwhelmed with advertisements that began to pop up in her social media feeds trying to get her to enroll in debt settlement companies, or offering help filing bankruptcy, with or without a lawyer. Katrina didn't know which of these tools to trust, and the court self-help website was overwhelming and full of confusing information that was hard to read on her mobile phone. Katrina is one of the estimated 71 million people in the United States with debt in collections and was one of almost a quarter of a million Californians sued for debt in 2018, almost all of whom have to navigate a state civil court system as unrepresented litigants against professional debt collection lawyers. Consumer debt collection cases comprise an increasing percentage of the dockets of most state civil courts in the United States. In California, over the last ten years, debt collection cases totaled an average of 20% of all cases filed, with debt cases rising to 37% of all civil filings in 2019. It is estimated that of the 71 million consumers who have debt in collections, 15% were sued in the last year. That means, according to the research of the Aspen Institute, an estimated 12 million people were sued across the United States to collect a consumer debt in the last year (most commonly credit card, medical debt, auto deficiency, or other consumer unsecured debt). The exact number of people sued on consumer debt cases in state courts each year is not known, because these data points are lost in a myriad of state court case management systems. Researchers and advocates know the exact number of businesses and consumers litigating in federal court, through the unified federal court management system PACER, and a rising number of data analytics companies, from Bloomberg to Lex Machina and Ravel Law, promise law firms and corporations ever-detailed information about judicial behavior and case trends. Also
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".