The King of the CASL: Canada’s Anti-Spam Law Invades the United States
Bibliographic record
Abstract
U.S. businesses periodically adjust their marketing practices to foreign law innovations. Several years ago, U.S. businesses emailing into Canada had to incorporate Canada’s Anti-Spam Law, otherwise known as CASL. Businesses that believed they email only U.S.-based customers likely dismissed CASL as not applicable. Others may never have heard of the law altogether. As this note discusses, CASL created a compliance conundrum for U.S. businesses. Since CASL methodically differs from the U.S. anti-spam law, CAN-SPAM, it may be in a business’s best interest to apply this law to its Canadian subset and not to the entire email population. Neither the law itself nor the regulatory agency, ISED, however, provides specified directives of data segmentation to discern a U.S. resident from a Canadian resident. Surely, generic domain extensions like “.com” do not point to a location on a map. Hence, even those businesses that comply with CASL for a subset of their email base are unlikely to achieve a one hundred percent compliance rate. This note shows how easy it is for a U.S. business, without realizing, to email into Canada. CASL's current low rate of enforcement and the pause of a private right of action mask the reality that without a viable foreign sender exemption, U.S. businesses may face legal risk. As Canada's government commenced CASL review, this note proposes a foreign sender exemption and beseeches Canada to create such an exemption prior to the reactivation of a private right of action.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.006 |
| Scholarly communication | 0.013 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.011 | 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".