Mystery shopping: demand-side phenomena in markets for personal plight legal services
Bibliographic record
Abstract
“Personal plight” is the sector of the legal services industry in which the clients are individuals, and the legal needs arise from disputes. This article proposes that competition among personal plight law firms is suppressed by three demand-side phenomena. First, consumers confront high search costs. Identifying competing law firms willing and able to provide the needed services often requires significant expenditure of temporal and psychological resources. Second, comparable price and quality information about firms is scarce for consumers. Both of these factors impede comparison shopping and reduce competitive pressure on firms. A third competition-suppressing factor is observed in tort legal service markets, where offerings are typically priced on a contingency basis. Contingency fees have relatively low salience to consumers, and this reduces consumers’ willingness to negotiate and comparison-shop on the basis of price. This analysis is supported by the author’s empirical research with Ontario personal plight lawyers as well as the existing literature. The article concludes by suggesting possible consequences of this analysis for regulatory policy.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 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".