Osgoode team of Anna Morrish and Andrea McPhedran place third overall in Canadian Client Consultation Competition
Bibliographic record
Abstract
First-year students at York University’s Osgoode Hall Law School were named third-place winners in the Canadian Client Consultation Competition held Feb. 24 and 25.\nStudents Anna Morrish and Andrea McPhedron came away with third overall when they travelled to the University of Alberta in Edmonton to compete. Pictured, from left, are Michael McNeely, Anna Morrish, Chief Justice Mary Moreau of the Alberta Court of Queens Bench (who was the keynote speaker at the banquet), Andrea McPhedran and Adam LaChance\nThe competition is designed to replicate a law office consultation and presents a client matter to two law students, who act as lawyers. Students conduct an interview with the “client”, and are expected to determine the relevant information from the client, explain the laws that are relevant and present the client with their legal options.\nThe client interview is followed up with a post-consultation period, when students analyze the interview and discuss next steps.\nSpecific criteria are used to evaluate students in the competition, and include the use of listening, questioning, planning, and analytical interview skills.\nA second Osgoode team, Michael McNeely and Adam LaChace, also participated in the competition.\nOsgoode students were joined by student mentors Joanne Raymond and Ben Fulton.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.004 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.162 | 0.020 |
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".