Privilege and Limitations: The Impact of Raising the Discoverability of Claims on Solicitor-Client Privilege
Bibliographic record
Abstract
The purpose of this article is to consider the waiver of privilege as between a plaintiff and his/her lawyer whenever a plaintiff invokes the concept of discoverability to delay the running of a limitation period that would otherwise bar a claim. This loss of privilege can, in turn, raise problems within the solicitor-client relationship and create complex issue of professional ethics for individual lawyers. This article argues that the legally correct understanding of this waiver of privilege in Canadian law is that it occurs automatically upon a plaintiff’s simple assertion of discoverability, and is not contingent upon a plaintiff claiming detrimental reliance on the advice or actions of counsel.
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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 | 0.037 | 0.206 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.011 | 0.042 |
| Scholarly communication | 0.016 | 0.024 |
| Open science | 0.004 | 0.018 |
| Research integrity | 0.011 | 0.014 |
| Insufficient payload (model declined to judge) | 0.013 | 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".