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Record W4409907621 · doi:10.1017/cls.2024.21

A School for Self-Represented Litigants: A People-Centred Approach to Access to Justice in Family Law

2024· article· en· W4409907621 on OpenAlexfundno aff
Jennifer Leitch, Dayna Cornwall

Bibliographic record

VenueCanadian Journal of Law and Society / Revue Canadienne Droit et Société · 2024
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Systems and Judicial Processes
Canadian institutionsnot available
FundersUniversity of California, IrvineUniversity of Windsor
KeywordsEconomic JusticeSociologyFamily lawLawCriminologyPsychologyPolitical science

Abstract

fetched live from OpenAlex

Abstract In recent years, there has been a dramatic increase in self-represented family law litigants. This increase permeates courts in a variety of jurisdictions. It is also clear that self-represented litigants (SRLs) disproportionately suffer more negative outcomes than represented parties. Therefore, the question now is what the responses are and should be to this phenomenon. Like many other facets of access to justice, the solutions in the family law context must be both diverse and tailored to the actual needs of those representing themselves in family law courts. This requires understanding the experiences and challenges faced by family law SRLs and ensuring that SRLs participate in the design of any responses developed. Such an endeavour is consistent with a people-centred approach to access to justice in which those directly impacted by the justice system are centred in the identification and development of viable solutions. One such project was the School for Family Litigants, an access to justice initiative designed by the National Self-Represented Litigants Project to help SRLs participating in family law litigation.

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)0.001
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 score0.805
Threshold uncertainty score0.939

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
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 head0.031
GPT teacher head0.305
Teacher spread0.274 · 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
Published2024
Admission routes1
Has abstractyes

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