Anti-Spam: A Comparative Appraisal of Canadian, European Union, and South African Regulatory Regimes: Lex Informatica Conference, 21st-23rd May 2008 Pretoria, South Africa
Bibliographic record
Abstract
1. Introduction The use of electronic technology including e-mails, cellular phones, the Internet and cyberspace as contracting media in the modern commercial world is forever gaining momentum (Tang, 2007, p 42). The huge opportunities created by electronic technology have been paralleled by some innovative marketing practices. Consumers are flooded with countless numbers of business offers from all over the world, almost on a daily basis and many of which are unsolicited (Tladi, 2008, p 178). Different terms have been used to refer to spam. Geissler (2004, pp 24-31) uses the term such as 'Unsolicited Bulk E-mail' (UBE); 'Unsolicited Commercial E-mail (UCE). From the onset the author would like to point out that the word 'spam' will, in this text, be used in its narrow sense to refer to unsolicited commercial communication (UCC). According to Polanski (2007, p 403), unsolicited commercial communications, spam as it is often called, has become a global plague. The communication is regarded as 'unsolicited' because there exist no prior relationship the recipient and the sender to justify such communication. Moreover, the recipient never explicitly agreed to receive such communication (generally, Solkin, 2001, pp 325-384). Put simply, the recipient has not given permission for the unsolicited communication to be sent to him or her. There is a continuing need for consumers to be protected from unwanted or malicious spam by every national jurisdiction through more effective means, including more effective legislative intervention. This paper compares and examines the legislative and regulatory systems of spam in Canada, the European Union, and South Africa. The key issue in this survey is how these jurisdictions deal with the problem of errant spamming. Minimal or non-government intervention policy in favour of industry self-regulation, which was once the preferred method in Canada, for example, may prove to be ineffective. Since the requirements for the presentation of this paper does not allow an in-depth enquiry into all the measures designed to combat spam, the author will not deal with various technical, educational and administrative efforts to prevent spam. 2. Problems Created by Spam It has become increasingly difficult to tolerate Spam, and to control and prevent it, even by using the most sophisticated of Spam filters. The spammers, too, have become sophisticated and may easily bypass these filters, even the revered Optical Character Recognition (OCR) technology, which has been created to translate pictorial or graphical Spam into computer fonts (Polanski, 2007, p 406). The problems created by Spam and the costs associated with combating it are clearly described as follows: (a) the recipient pays far more, in time and trouble as well as money, than the sender does (unlike unrequested advertising through the postal service); (b) the recipient must take the time to request removal from the mailing list, and most spammers claim to remove names on request but rarely do so; (c )many spammers use intermediate systems without authorization to avoid blocks set up to avoid them; (d) many Spam messages are deceptive and partially or entirely fraudulent; and (e) the recipient ends up with the problem of technological Spam filters that also block non-spam messages (Levine, ; Delio, 2000, ). In South Africa the spam is reported to cost business between R7 billion and R13 billion yearly in lost productivity (Tladi, 2008, p 183). 3. Spam Regulatory and Legislative Measures 3.1 South Africa 3.1.1 Spam Specific Legislative Interventions E-commerce in South Africa is governed primarily by the Electronic Communications and Transactions Act (ECTA) 25 of 2002, which came into force on 30 August 2002, as South Africa's first comprehensive e-commerce legislation (Sibanda, 2008, p 321). …
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".