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Record W2943076830

Views of Ontario Lawyers on Family Litigants Without Representation

2012· article· en· W2943076830 on OpenAlexaboutno aff
Rachel Birnbaum, Nicholas Bala

Bibliographic record

VenueQSpace (Queen's University Library) · 2012
Typearticle
Languageen
FieldArts and Humanities
TopicHermeneutics and Narrative Identity
Canadian institutionsnot available
Fundersnot available
KeywordsRepresentation (politics)SociologyEpistemologyPsychologyPolitical scienceLawPhilosophy
DOInot available

Abstract

fetched live from OpenAlex

The increasing number of family litigants without legal representation poses significant challenges for lawyers, litigants and the justice system. This paper reports on a survey of 335 Ontario family lawyers about their experiences with litigants without legal representation and related access to justice issues. These lawyers report an increase in the number of family litigants without lawyers. They attribute this increase principally to lack of eligibility for legal aid and an inability to afford counsel, but also recognize such factors as a perception that lawyers increase the expense and complexity of resolving cases, and litigants’ belief that they can adequately represent themselves. Participants also recognized that some litigants have a desire to personally confront a former partner. Overall, lawyers report that cases involving litigants without representation take longer and are more costly for their clients to resolve; they also report worse outcomes for many of those without lawyers.

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)0.004
metaresearch head score (Gemma)0.012
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.172
Threshold uncertainty score0.346

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.012
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0230.008
Scholarly communication0.0050.002
Open science0.0010.004
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0090.000

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 head0.035
GPT teacher head0.220
Teacher spread0.186 · 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 designQualitative
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
Published2012
Admission routes1
Has abstractyes

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