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Record W2012229471 · doi:10.1017/s0003975613000039

Democracy, Courts and the Information Order

2013· article· en· W2012229471 on OpenAlexaff
Gillian K. Hadfield, Dan Ryan

Bibliographic record

VenueEuropean Journal of Sociology · 2013
Typearticle
Languageen
FieldSocial Sciences
TopicLaw in Society and Culture
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsEntitlement (fair division)PlaintiffPolitical scienceDemocracySettlement (finance)Order (exchange)LawPoliticsLaw and economicsSociologyBusinessEconomics

Abstract

fetched live from OpenAlex

Abstract Conventional wisdom about civil litigation, both among scholars and political actors, holds that abuse of the legal process is common, that there is too much litigation, that it is “all about the money”, and that “a bad settlement is better than a good trial”. This constellation of attitudes that emphasize the economic function of law suggests that courts are an expensive conflict resolution mechanism of last resort and that their use would be minimized in a healthy market-based democracy. In this paper we apply a new sociological framework to understand the meaning and function of civil litigation in a democratic society. We focus in particular on the democratic function of the informational characteristics of litigation that require substantial disclosure and engagement between plaintiff, defendant and third parties. Instead we examine the role courts play in the maintenance and attainment of a social information order – norms and legal rules governing the sharing and withholding of information that depend on and constitute particular status relationships between actors (Ryan 2006). Using interviews and surveys of family members of victims of 9/11 we develop a theory of the lived experience of entitlement to information in Anglo-American legal settings with suggestions of how these ideas might translate to civil law systems.

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)0.002
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 score0.546
Threshold uncertainty score0.454

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.009
GPT teacher head0.242
Teacher spread0.233 · 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

Citations0
Published2013
Admission routes1
Has abstractyes

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