Zealous Advocacy or Exploitative Shakedown?: The Ethics of Shoplifting Civil Recovery Letters
Bibliographic record
Abstract
Twenty years ago, Canadian retailers imported the American practice of sending letters to alleged shoplifters and their parents demanding the payment of several hundred dollars as “civil recovery.” In the United States, this practice is backed by state legislation that explicitly provides retailers with a statutory cause of action against shoplifters. In Canada, however, no similar legislation exists. Instead, Canadian retailers have attempted to justify their “civil recovery” claims by relying on common law torts. In order to give their demands increased authority, many retailers retain lawyers to send out their “shoplifting civil recovery letters” (“SCRLs”). This article supplements existing critiques of lawyers who send SCRLs and makes the case for greater law society regulation through a detailed analysis of the common law claims advanced in SCRLs and a consideration of whether advancing such claims violates a lawyer’s ethical obligations. This article concludes that lawyers who send SCRLs act unethically by advancing legal and factual claims for which there is no good faith basis. In order to combat this problem, law societies should take action by publishing practice directions on the topic of SCRLs and by disciplining lawyers who violate their professional obligations when sending SCRLs.
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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 | 0.018 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.120 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.016 | 0.013 |
| Insufficient payload (model declined to judge) | 0.002 | 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".