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Record W1525519862

EXPLORING ECONOMIC AND DEMOCRATIC THEORIES OF CIVIL LITIGATION: DIFFERENCES BETWEEN INDIVIDUAL AND ORGANIZATIONAL LITIGANTS IN THE DISPOSITION OF FEDERAL CIVIL CASES

2005· article· en· W1525519862 on OpenAlexaff
Gillian K. Hadfield

Bibliographic record

VenueSSRN Electronic Journal · 2005
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsAdjudicationPolitical scienceDispute resolutionPoliticsIncentiveDemocracyLawLaw and economicsEconomic JusticeSociologyEconomics
DOInot available

Abstract

fetched live from OpenAlex

Recent efforts to assess whether or not the trial is vanishing from the civil justice system, have thus far not drawn distinctions between cases in which the market efficiency function of the legal system is in play and those in which democratic and political functions are in play. As Marc Galanter famously set out thirty years ago in his seminal work on why the haves come out ahead and as current studies of the legal profession confirm, however, we should expect that there are significant differences in how corporations, organizations, governments and private individuals fare in our legal system: these different entities bring different resources to bear and they face different repeat versus one-shot incentives. Normatively, the issues at stake in our understanding of what is happening to civil cases and the efforts to craft alternatives to traditional civil litigation require that we differentiate between litigants, between legal functions, and between the different goals of our legal system. It may be that the disappearance of public civil trials to resolve commercial contract disputes is of no consequence; indeed it may be an efficient response to the increasing cost of the public system. The same cannot be said of the disappearance - if it is a real phenomenon - of public adjudication of civil rights or the claims of individuals about the misconduct of public or corporate actors. Private and confidential dispute resolution may be perfectly appropriate and something to be promoted in the resolution of family disputes, whereas it may be inappropriate in the resolution of patent disputes in which two corporations may bargain over the division of monopoly rents or in the resolution of disputes between the state and citizens about how electoral districts are determined. If judicial resources are strained by caseloads, which litigants are flooding in - corporate or individual? And if rationing is required, if an attempt to reduce the number of cases to which judges and courts devote their efforts is required, which cases should be diverted into private dispute resolution and which should be retained for public adjudication? In this paper I present preliminary data on the differences between individual and organizational litigants in the disposition of federal civil cases. This paper follows on an earlier paper in which I developed a methodology for increasing the value of the database created by the Administrative Office of the US Courts. Here I endeavor to show the differences between individual and organizational litigants in the rate at which cases are abandoned, defaulted, adjudicated without a trial, adjudicated with a trial, or settled. The results show substantial differences in cases based, primarily, on plaintiff rather than defendant type. I find individual plaintiff cases are substantially more likely to be determined by an adjudication - especially a non-trial adjudication - than are organizational plaintiff cases. I also find evidence that organizational plaintiffs - against either individual or organizational defendants - are substantially more likely to settle their cases rather than to have them decided either by trial or non-trial adjudication.

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 imitation

Not 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.

metaresearch head score (Codex)0.027
metaresearch head score (Gemma)0.094
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.027
Threshold uncertainty score0.141

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0270.094
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0060.003
Science and technology studies0.0070.027
Scholarly communication0.0120.012
Open science0.0020.006
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0110.001

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.

Opus teacher head0.038
GPT teacher head0.215
Teacher spread0.178 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations14
Published2005
Admission routes1
Has abstractyes

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