Bibliographic record
Abstract
If a lawyer fails to prepare his client for mediation, and bullies her into a settlement, a court may find the lawyer negligent and award damages to the client amounting to the difference between what she settled for and what she likely would have obtained in court (or arbitration). That is what happened in Raichura v Jones, 2020 ABQB 139, a recent decision from the Alberta Court of Queen’s Bench. In this case, the lawyer was ordered to pay damages of $131,939. In other words, this case is a lawyer’s nightmare. The facts may be uncommon, but the decision includes important warnings. The case has naturally provoked interest from the family law bar and has already been blogged about by Lorne Wolfson here, and by Aaron Franks and Michael Zalev in the June 1, 2020 edition of This Week in Family Law (paywall). As both blogs pointed out, the decision is being appealed. My primary interest with Raichura v Jones is the resounding message that lawyers should not bully their clients into a settlement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.028 | 0.017 |
| Insufficient payload (model declined to judge) | 0.032 | 0.015 |
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".