Fighting Spam. How Stringent is the Canadian Legal Arsenal. An Analysis in the Light of the U.S. CAN-SPAM Act
Bibliographic record
Abstract
Following several countries, Canada recently passed Canada’s Anti-Spam Legislation (CASL), in an attempt to tackle spam. The law aims to ‘‘protect Canadians while ensuring that businesses can continue to compete in the global marketplace”. For this purpose, CASL prohibits not only the sending of commercial electronic messages without consent, but also any alteration of transmission data in the course of a commercial activity. Moreover, the Act disallows the installation of a computer program on another person’s computer system and the sending of commercial electronic messages following the installation. These three activities are prohibited unless the author or initiator has obtained the recipient’s prior consent, either express or implied. This opt-in approach contrasts with the U.S. CAN- SPAM Acts opt-out regime, in force since 2004, which is known to offer to senders the chance to initiate contact and to recipients the option to unsubscribe or reject any subsequent commercial electronic message. Our paper intends to demonstrate that, notwithstanding the apparent difference in their respective approach, CASL and U.S. CAN-SPAM Act remain fundamentally similar in practical effect. This resemblance is good news, considering the profile and proximity of Canadian and American e-commerce economies. Thus, in spite of its detail and complexity, CASL may not be the most stringent anti-spam act as claimed, certainly not with the challenges related to its implementation and enforcement.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.029 | 0.020 |
| Scholarly communication | 0.028 | 0.013 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.009 | 0.008 |
| Insufficient payload (model declined to judge) | 0.010 | 0.003 |
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".