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Record W7053712176

Where Are We Now? Accessing the Current Ontario Family Justice System

2021· article· en· W7053712176 on OpenAlexaboutno aff

Bibliographic record

VenueScholarship@Western (Western University) · 2021
Typearticle
Languageen
FieldEngineering
TopicPlasma Diagnostics and Applications
Canadian institutionsnot available
Fundersnot available
KeywordsEconomic JusticeWork (physics)Criminal justiceAction (physics)Family law
DOInot available

Abstract

fetched live from OpenAlex

Is the current family justice system more accessible than ever before? This paper considers the significant changes that have been made to the Ontario family justice system in recent years, including those made as a result of the COVID-19 pandemic, to determine if the “fundamental overhaul” and “bold innovation” called upon by the national Action Committee has occurred, bringing Ontario closer to a more accessible family justice system.\nSeveral prominent legal scholars have identified access to family justice in Canada as a crisis and have made strongly worded recommendations on how the family justice system could be more accessible. As a result, small but significant reform was implemented. In response to the COVID-19 pandemic, Ontario's family justice system made additional changes, including technological advancements to improve access to justice. Were these the “fundamental” and “bold” changes needed to end the access to justice crisis in Ontario family law?\nLooking at key reports and articles on access to family justice reform, as well as changes made in recent years, including changes made as a result of the COVID-19 pandemic, this paper argues that the current Ontario family justice system is more accessible, but more work needs to be done to ensure we do not continue to leave the most vulnerable behind.

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)0.002
metaresearch head score (Gemma)0.007
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.077
Threshold uncertainty score0.556

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0150.008
Scholarly communication0.0070.005
Open science0.0020.003
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0160.001

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 head0.091
GPT teacher head0.292
Teacher spread0.201 · 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 designNot applicable
Domainnot available
GenreOther

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
Published2021
Admission routes1
Has abstractyes

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