Sandbagging in Canadian Law and Practice
Bibliographic record
Abstract
In the language of mergers and acquisitions, “sandbagging” refers to situations in which a buyer brings an indemnification claim for a breach of the seller’s representations and warranties that the buyer was aware of prior to closing. In contractual negotiations, sellers often argue that sandbagging allows unscrupulous buyers to reduce the agreed-upon purchase price by abusing the indemnification mechanism. For their part, buyers argue that sandbagging can protect their legitimate contractual interests, particularly in cases where a seller seeks to dispute a valid indemnification claim. This tension between buyers and sellers is heightened by the fact that the legal status of sandbagging in Canada is unclear. Although transactional lawyers generally believe that courts will enforce clear “pro” or “anti” sandbagging provisions—i.e., contractual provisions that expressly permit or forbid sandbagging—whether courts will permit sandbagging in the face of contractual silence is uncertain. In light of this uncertainty, this article argues that courts should adopt a clear “pro-sandbagging” default rule in cases where the acquisition agreement is silent. Although sandbagging is controversial, this article argues that there are important practical and economic reasons to allow buyers to bring indemnification claims for contractual breaches of which they allegedly had knowledge. My central argument is that a pro-sandbagging default rule is economically efficient in that it (1) facilitates the informational purpose of contractual representations and warranties and (2) reduces ex post litigation costs. By interpreting the terms of M&A agreements strictly—thereby allowing sandbagging—courts can increase legal certainty while facilitating the production of valuable information, ultimately benefiting both buyers and sellers.
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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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.035 | 0.019 |
| Scholarly communication | 0.011 | 0.004 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.007 | 0.006 |
| Insufficient payload (model declined to judge) | 0.018 | 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".