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Record W2859687744 · doi:10.1080/09695958.2018.1490292

Mystery shopping: demand-side phenomena in markets for personal plight legal services

2018· article· en· W2859687744 on OpenAlexaffabout
Noel Semple

Bibliographic record

VenueInternational Journal of the Legal Profession · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Windsor
Fundersnot available
KeywordsSalience (neuroscience)NegotiationCompetition (biology)BusinessContingencyMarketingQuality (philosophy)Service (business)TortCompetition lawLiabilityLawEconomicsMarket economyFinance

Abstract

fetched live from OpenAlex

“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.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.470

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.023
GPT teacher head0.261
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2018
Admission routes2
Has abstractyes

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