May an Attorney Agree to Reimburse the Client if the Client Loses: A Study
Bibliographic record
Abstract
This article studies whether an attorney may contract with a client to reimburse the client for the attorney fees and costs of the opponent if the client loses. I survey the relevant laws of the United States, Canada, and Australia on this issue. Canada and Australia, countries with fee shifting rules similar to Korea, generally allow this. Next, I argue that such a provision does not violate Korean Attorney-at-law Act nor the Korean legal ethics. There is no prior disciplinary proceeding result that I can find on this issue. Although the Attorney-at-law Act and the Korean legal ethics both require an attorney to maintain her dignity, the duty to maintain dignity was not interpreted to ban such a provision. Because a lawyer is in a better position to know whether a given case is likely to win, and because the lawyer can always not agree to a provision if the lawyer does not get enough information from the client about the case, the lawyer should be given the freedom to agree to such a provision if it is need to get a case to go forward. If it is not feasible to allow such a provision generally, then, considering that Securities-related Class Action Act Provision 11 was enacted to have an institutional investor monitor the lawyer for the class, and considering that an institutional investor would not agree to be a lead plaintiff because of the risk of the fee being shifted if the plaintiff loses, an institutional investor should be allowed to hedge such a risk by allocating at least some of the risk to the lawyer by contract.
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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 | 0.016 | 0.062 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.004 | 0.005 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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; 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".