BUSINESS DOMINANCE IN THE “PEOPLE’S COURT”: AN EMPIRICAL ASSESSMENT OF BUSINESS ACTIVITY IN THE TORONTO SMALL CLAIMS COURT
Bibliographic record
Abstract
This article examines business dominance in small claims courts from four perspectives: party entity, party relationship, claim subject matter, and business plaintiff success. Using data collected from the Toronto Small Claims Court, it compares business activity before and after the monetary limit rose from CDN $10,000 to CDN $25,000. Although most claims are brought by business plaintiffs against individual defendants, business dominance did not worsen following the limit increase. In some ways, it actually receded. Individual defence rates rose, and business plaintiff success fell. In sum, capping monetary limits is not recommended to reduce business activity in small claims courts. Dans cet article, l’auteure étudie la dominance des entreprises devant les cours de petites créances sous quatre angles : celui de la personne morale, celui des rapports des parties entre elles, celui de l’objet revendiqué et celui du succès de l’entreprise demanderesse. En se servant de données provenant de la cour des petites créances de Toronto, l’auteure compare les proportions d’instances introduites par les entreprises avant et après le passage de 10 000 $ à 25 000 $ CDN de la limite pécuniaire des réclamations. Bien que la plupart des instances soient introduites par des entreprises demanderesses contre des particuliers défendeurs, la dominance des entreprises ne s’est pas accentuée après le relèvement de la limite pécuniaire des réclamations. À certains égards, cette dominance a en fait reculé. Les moyens de défense des particuliers se sont accrus et le taux de succès des entreprises demanderesses a baissé. En conséquence, il n’est pas recommandé de plafonner la limite pécuniaire des réclamations pour réduire la place que prennent les entreprises devant les cours de petites créances.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.008 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".