Summary Legal Advice Services in Alberta: Survey Results from the First Two Years of Data Collection
Bibliographic record
Abstract
The Alberta Law Foundation and the community legal clinics in Alberta have implemented a project to evaluate the summary legal advice services provided by the clinics. Summary legal advice involves volunteer lawyers providing up to 60 minutes of legal advice to low income individuals who are generally handling their legal problems on their own. A logic model was developed to articulate the inputs, activities, outputs, and intended outcomes of the services, and survey instruments were designed to assess the outcomes. The clinics participating in the study are: Calgary Legal Guidance; Edmonton Community Legal Centre; Central Alberta Community Legal Clinic in Red Deer; and Lethbridge Legal Guidance. The overall goal of the project is to build evaluation capacity within the clinics to enable them to carry out their own evaluations, thus providing them with the data needed to make evidence-based decisions to improve their legal advice services. The goal of the evaluation is to examine the extent to which the intended outcomes identified in the logic model are being achieved, and to assess what type of adjustments may be necessary to better achieve the intended outcomes. The Canadian Research Institute for Law and the Family was asked to assist the project by reviewing and revising the proposed data collection instruments, establishing data collection procedures and documenting them in a user-friendly manual for clinic staff, and periodically analyzing the aggregate data from the four participating clinics.
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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 | 0.006 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".