Bibliographic record
Abstract
<p dir="ltr">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.</p><p><br></p><p><br></p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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; both teacher heads agree on what is shown here.
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".