Law Stretched Thin: Access to Justice in Rural America
Bibliographic record
Abstract
About two percent of small law practices in the United States are in small towns and rural areas, a figure greatly disproportionate to the nearly twenty percent of the population living in those places. This mismatch leaves many rural legal needs unmet. In 2013, responding to the well-documented lawyer shortage in the upper Great Plains, South Dakota became the first state to offer subsidies to lawyers who practice in rural areas through a program called Project Rural Practice. This article takes the opportunity invited by Project Rural Practice — and this symposium issue about it — to discuss a range of access to justice issues in rural places. Those providing rural legal services face challenges, of course, the most obvious being the struggle for economic viability, along with others generally associated with small firms and solo practice. Rural lawyers also face socio-spatial barriers to professional development and networking opportunities, and the lack of anonymity associated with rural places creates both ethical and economic conflicts of interest. Beyond the challenges facing rural lawyers, we recognize that individuals residing in rural America encounter obstacles to seeking legal services. These obstacles include affordability, confidentiality, and even inability to actually get to courthouses and other legal institutions and actors. Compounding matters, rural denizens are associated with an ethic of law avoidance. Persistently high poverty rates in rural America aggravate the challenge because poor people are less likely to have their legal needs met, wherever they live. We situate our discussion of rural legal practice within the larger body of access to justice scholarship. While we recognize Project Rural Practice as a strong step in support of rural legal practice, and therefore in support of rural communities, we argue that it should be supplemented by programs aimed at helping those least likely to get the legal assistance they need, especially low-income and other vulnerable populations. We also advocate additional supports for lawyers willing to provide such assistance. Among other issues, we discuss the roles that paralegals, technology, and the broader social-service, non-profit community can play in responding to rural legal needs. We pay particular attention to pro bono publico in the rural context. Lastly, our article includes a modest comparative component, surveying available information about rural lawyering and access to justice in other nations, most notably Canada and Australia. We conclude that achieving robust access to justice requires the attention and effort of an entire community. Enabling access to justice should include partnerships with a wide array of service providers who can meet non-legal needs while also helping those confronting problems to identify when legal assistance could be of use. Given the dearth of rural lawyers, the surfeit of urban lawyers, and the preference of many attorneys to deliver pro bono services away from their usual practice settings, we also see collaboration among lawyers across and along the rural-urban continuum as critical pieces of the rural access to justice puzzle. We believe not only that these myriad collaborations can help rural residents get the legal assistance they need to solve their most pressing problems, but that the collaborations also hold promise for ameliorating the structural deficits that afflict entire rural communities.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.009 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 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".