MétaCan
Menu
Back to cohort
Record W3103222853

Lawyers and Court Representation of Organized Pseudolegal Commercial Argument [OPCA] Litigants in Canada

2018· article· en· W3103222853 on OpenAlexaboutno aff
Donald Netolitzky

Bibliographic record

VenueSSRN Electronic Journal · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicLaw, Economics, and Judicial Systems
Canadian institutionsnot available
Fundersnot available
KeywordsArgument (complex analysis)LawRepresentation (politics)OrthodoxyPolitical scienceIdeologyFunction (biology)SociologyHistoryMedicine
DOInot available

Abstract

fetched live from OpenAlex

Litigants who advance unorthodox law-like concepts, “pseudolaw”, have appeared in Canadian courts for several decades. Courts reject pseudolaw as vexatious and an abuse of court. The motivations and characteristics of pseudolaw litigants differ. Some are principally results-oriented, seeking to use pseudolaw for personal advantage. Others ground their use of pseudolaw on conspiratorial, paranoid, and ideological beliefs. While most litigants who employ pseudolaw are unrepresented, a significant fraction retained lawyers for some or all of their proceedings. The lawyer’s functions also vary. Some are retained to conduct ‘damage control’ after pseudolaw was used but then abandoned. Other lawyers explored dubious but arguable pseudolaw, or were temporarily retained for a specific objective, such as to obtain bail. A small number of rogue lawyers have entirely rejected legal orthodoxy and fully embraced pseudolaw, arguing these concepts for their clients and even themselves. Some pseudolaw litigants for tactical advantage use a flexible litigation strategy, and alternate between representation by a ‘conventional’ lawyer, a rogue lawyer, and self-representation. This poses a unique challenge to court function and litigation management.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.427
Threshold uncertainty score0.564

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 communication0.0000.000
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 head0.014
GPT teacher head0.213
Teacher spread0.199 · 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 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

Citations7
Published2018
Admission routes1
Has abstractyes

Explore more

Same venueSSRN Electronic JournalSame topicLaw, Economics, and Judicial SystemsFrench-language works237,207