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Record W2895866880 · doi:10.60082/0829-3929.1310

A Current Assessment of Legal Aid in Ontario

2018· article· en· W2895866880 on OpenAlexafffundvenueabout
Frederick H. Zemans, Justin Amaral

Bibliographic record

VenueJournal of Law and Social Policy · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicLegal Education and Practice Innovations
Canadian institutionsYork University
FundersGovernment of CanadaU.S. Department of Justice
KeywordsLegal serviceService providerGovernment (linguistics)PovertyBusinessPer capitaEconomic JusticeCertificateService (business)Inflation (cosmology)DutyPublic economicsEconomic growthEconomicsPolitical scienceLawMarketingSociology

Abstract

fetched live from OpenAlex

This article explores the development of legal aid services in Ontario over the past two decades. The authors find that per capita inflation-adjusted spending on legal aid services by the federal government has been in long-term decline (albeit with periodic upturns) with resulting negative impacts on access to justice for those in need of legal assistance. At the provincial level, since cuts made in the mid-1990s, financial eligibility guidelines have remained out of line with real measures of poverty, such as Statistics Canada’s low-income cut-offs, and per capita funding has only recently increased. The mix of legal aid service providers in the province consists of fewer certificate lawyers and per-diem duty counsel than in the past. The recent introduction of new service providers and technological innovations—driven by a desire to both reduce costs and improve client services—may have produced some positive outcomes, however, research has not yet established whether new service providers and new technologies are simply backstopping, rather than augmenting, prior levels of service. The authors conclude that there is a need for more research on the developments they describe and on how legal aid services can be enhanced and expanded in the future.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.933
Threshold uncertainty score0.733

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.079
GPT teacher head0.480
Teacher spread0.401 · 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 designTheoretical or conceptual
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

Citations2
Published2018
Admission routes4
Has abstractyes

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