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Record W2898100169 · doi:10.11575/prism/32514

Self-represented Litigants in Family Law Disputes: Views of Alberta Lawyers

2012· article· en· W2898100169 on OpenAlexaboutno aff
L.D. Bertrand, J.J. Paetsch, Nicholas Bala, Rachel Birnbaum

Bibliographic record

VenueOpen MIND · 2012
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsLawFamily lawPolitical science

Abstract

fetched live from OpenAlex

The current study modified the instrument in a survey by Birnbaum & Bala (2012) with Ontario lawyers to obtain the experiences with and opinions of Albertans regarding self-representation. The Survey on Experiences with Self-represented Litigants was a web-based survey that was conducted with a sample of family law lawyers in Alberta. The sample was compiled from various lists maintained by the Canadian Research Institute for Law and the Family (CRILF) and e-mail addresses were verified by individual lawyers’ and law firms’ web sites and the 2012-13 Alberta Legal Telephone Directory. An initial invitation to complete the survey along with a link to the web site containing it was e-mailed to 174 family law lawyers across Alberta on June 13, 2012 with a request that they complete the survey by July 6, 2012. A reminder e-mail to the complete sample was sent on June 26th and the survey was closed to new responses on July 31, 2012. A total of 73 valid surveys were completed, resulting in a response rate of 42%. The survey contained background questions regarding respondents’ experience in the family law area in general, as well as their experiences with self-represented litigants in the family law area. In addition, participants were asked their views on alternatives to the traditional family law model, their opinions of parenting education workshops, and their involvement in providing pro bono services in family law.

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)0.010
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 score0.983
Threshold uncertainty score0.529

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0040.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0090.004
Scholarly communication0.0040.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.141
GPT teacher head0.451
Teacher spread0.310 · 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

Citations5
Published2012
Admission routes1
Has abstractyes

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