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Record W7046093385

The Cost of Experiencing Everyday Legal Problems Related to Loss of Employment and Loss of Housing

2017· article· en· W7046093385 on OpenAlexaboutno aff

Bibliographic record

VenueeYLS (Yale Law School) · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeJob lossLegal serviceLegal actionCivil rights
DOInot available

Abstract

fetched live from OpenAlex

In 2014, the Canadian Forum on Civil Justice (CFCJ) conducted a national survey to determine the costs, extent and consequences of serious civil and family justice problems experienced by the Canadian public. Findings from the CFCJ’s national “Everyday Legal Problems and the Cost of Justice in Canada” survey (Cost of Justice survey) confirm that civil and family legal problems result in significant monetary costs to the individuals who experience them as well as to the publicly funded services that individuals access for support when dealing with legal problems. With Canadians spending almost as much to resolve their legal problems as they do on food for a year, it is hardly surprising that for many people, the financial burdens associated with experiencing a serious civil or family justice problem cause or contribute to other legal, social, family, health and personal problems . A significant number of Canadians also report losing their job and/or their housing as a direct consequence of one or more legal problems.

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)0.001
metaresearch head score (Gemma)0.013
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.698
Threshold uncertainty score0.608

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.013
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.003
Science and technology studies0.0040.002
Scholarly communication0.0020.001
Open science0.0010.003
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0060.000

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 head0.353
Teacher spread0.318 · 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 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

Citations0
Published2017
Admission routes1
Has abstractyes

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