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Record W1508286664 · doi:10.60082/2817-5069.2791

No Lawyer for a Hundred Miles?: Mapping the New Geography of Access of Justice in Canada

2015· article· en· W1508286664 on OpenAlexaffvenueabout
Jamie Baxter, Albert Yoon

Bibliographic record

VenueOsgoode Hall law journal · 2015
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicOccupational and Professional Licensing Regulation
Canadian institutionsUniversity of TorontoDalhousie University
Fundersnot available
KeywordsEconomic JusticeScope (computer science)Distribution (mathematics)LocalityPolitical scienceLegal educationLegal professionLegal serviceRural areaLawGeographyPublic relationsSociologyPublic administration

Abstract

fetched live from OpenAlex

Recent concerns about the geography of access to justice in Canada have focused on the dwindling number of lawyers in rural and remote areas, raising anxieties about the profession’s inability to meet current and future demands for localized legal services. These concerns have motivated a range of policy responses that aim to improve the education, training, recruitment and retention of practitioners in underserved areas. We surveyed lawyers across Ontario to better understand their physical proximity to clients and how, if at all, that proximity promotes access to justice. We find that lawyers’ scope of practice varies based on a number of factors, and in several areas of law lawyers serve clients beyond their immediate locality. Our results suggest that debates about the geography of access should be premised on the goal of territorial justice as an equitable distribution of legal services rather than a narrower emphasis on the equal distribution of 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)0.001
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.102
Threshold uncertainty score0.738

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0050.012
Science and technology studies0.0080.004
Scholarly communication0.0060.002
Open science0.0020.004
Research integrity0.0010.001
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 head0.112
GPT teacher head0.266
Teacher spread0.154 · 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

Citations13
Published2015
Admission routes3
Has abstractyes

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