Reducing The “Justice Gap” Through Access to Unbundled Legal Services: Utilizing an A2J Measurement Framework to Measure Unbundling Effectiveness
Bibliographic record
Abstract
The ever-growing disparity between the cost of legal services and Canadians’ ability to pay for those services is known as the “justice gap.” As that gap widens, stakeholders must look for innovative ways to address it. In recent years, the potential for lawyers to offer unbundled legal services [ULS] through limited scope retainers has received considerable commentary as a “person” or “user-centred” justice tool, largely based on increasing affordability. ULS may expand access to individuals that cannot afford full representation but do not qualify for government-funded legal aid. However, empirical research on ULS as an access-to-justice (A2J) tool in Canada has only begun. This article contributes to the growing discourse on measuring ULS and is novel in Canada in its examination of ULS effectiveness as an A2J tool in reference to an A2J Measurement Framework and sample survey data from a ULS pilot project in Saskatchewan, Canada. We also identify what we do not yet know about ULS efficacy and why we should care about these unknowns. We caution against some of the generalizations currently made in the literature about which individuals are best suited for ULS. Finally, we conclude with ideas on how to continue studying these unknowns in reference to the framework for more efficient evaluation and comparative ULS data across jurisdictions. Utilizing the framework to measure ULS effectiveness across jurisdictions could help determine whether such initiatives are working from an A2J view and making a difference in the overall movement to reduce the justice gap.
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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 | 0.055 | 0.105 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.012 |
| Science and technology studies | 0.010 | 0.014 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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.
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".