No Lawyer for a Hundred Miles? Mapping the New Geography of Access of Justice in Canada
Bibliographic record
Abstract
Abstract\nRecent 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. French Abstract\nLa géographie changeante de l’accès à la justice au Canada entraîne aujourd’hui de nouvelles préoccupations, car de moins en moins d’avocats fréquentent les campagnes et les régions éloignées du pays, ce qui fait douter de l’aptitude de la profession à remplir les besoins actuels et futurs des services juridiques en région. Ces préoccupations motivent une gamme de politiques qui ont pour objectif d’améliorer la conscientisation, la formation, le recrutement et la rétention de praticiens dans les régions moins bien desservies. Nous avons interrogé des avocats un peu partout en Ontario afin de mieux comprendre leur proximité physique à leur clientèle et la manière, si c’est le cas, dont cette proximité favorise l’accès à la justice. Nous avons découvert que le champ d’activité des avocats varie en fonction d’un certain nombre de facteurs et que, dans plusieurs domaines juridiques, les avocats servent une clientèle dans un périmètre plus étendu. Nos résultats suggèrent que le débat sur la géographie de l’accès à la justice devrait avoir pour prémisses l’objectif de faire reposer la justice territoriale sur une distribution équitable des services juridiques plutôt que sur le concept plus étroit d’une distribution uniforme des avocats.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.009 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".