Unintended Consequences: The Regressive Effects of Increased Access to Courts
Bibliographic record
Abstract
Small claims courts enable parties to resolve their disputes relatively quickly and cheaply. The court's limiting feature, by design, is that alleged damages must be small, in accordance with the jurisdictional limit at that time. Accordingly, one might expect that a large increase in the upper limit of claim size would increase the court's accessibility to a larger and potentially more diverse pool of litigants. We examine this proposition by studying the effect of an increase in the jurisdictional limit of the Ontario Small Claims Court. Prior to January 2010, claims up to $10,000 could be litigated in the small claims court. After January 2010, this jurisdictional limit increased to include all claims up to $25,000. We study patterns in nearly 625,000 disputes over the period 2006–2013. In this article, we investigate plaintiff behavior. Interestingly, the total number of claims filed by plaintiffs does not increase significantly with the increased jurisdictional limit. We do find, however, changes to the composition of plaintiffs. Following the jurisdictional change, we find that plaintiffs using the small claims court are, on average, from richer neighborhoods. We also find that the proportion of plaintiffs from poorer neighborhoods drops. The drop‐off is most pronounced in plaintiffs from the poorest 10 percent of neighborhoods. We explore potential explanations for this regressive effect, including crowding out, congestion, increased legal representation, and behavioral influences. Our findings suggest that legislative attempts to make the courts more accessible may have unintended regressive consequences.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.043 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 0.001 |
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 source (direct Gemma or distilled Codex), 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".