Unsolicited e-mail: taking technology to court, a comparative analysis between technology and law
Bibliographic record
Abstract
E-mail has always suffered from some degree of abuse. Yet the gargantuan spam growth rates of the past few years have led governments and international organisations to engage in global efforts to fight the flood of unwanted messages. This thesis compares solutions provided by Technology and Law. First, a technological analysis examines the e-mail system and crystallises the shortcomings exploited by spammers. Second, anti-spam and anti-virus legislation in Canada, the U.S.A., Australia and the EU are scrutinized and compared with one another. Finally, the main comparison between Technology and Law suggests that neither Technology nor Law are suited, necessary or efficient for each anti-spam solution. While Technology focuses its solutions around the receiver and the e-mail network, the solutions of Law are distributed more evenly and range throughout the entire e-mail system from the sender to the receiver. Technology and Law need improved synchronising at more than one interface.
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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.017 |
| Scholarly communication | 0.018 | 0.024 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".