Measuring the Impacts of Representation in Legal Aid and Community Legal Services Settings: Considerations for Canadian Research
Bibliographic record
Abstract
There is currently a gap in Canadian empirical research examining the impacts of legal representation in legal aid and clinic settings. This article advocates for addressing the research gap and suggests how such research could be pursued. Empirical data is crucial to making the case for ongoing investments in publicly funded legal assistance and to ensuring the effectiveness of such assistance. Yet current research, mainly from American studies, tends to focus narrowly on litigation outcomes. This leaves many aspects of the impact of legal representation unclear, particularly regarding service delivery for vulnerable and marginalized clients. Research must examine clients’ own experiences and perspectives of legal processes so as to better reflect the complex relationship between legal representation and justice.
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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 | 0.134 | 0.235 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.017 | 0.027 |
| Science and technology studies | 0.020 | 0.013 |
| Scholarly communication | 0.023 | 0.013 |
| Open science | 0.008 | 0.015 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.008 | 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".