Turning Worms: Some Thoughts on Liabilities for Spreading Computer Infections
Bibliographic record
Abstract
Two aspects of the virus/worm liability problem are of particular note. The first is how tightly the Internet binds together many possible defendants; those who build and run it, those who populate it with increasingly complex electronic commerce Web sites, those who provide terminal software, those who send electronic mails, those who design its security algorithms, those who insure it, and those who hack it, amongst others.\nThe second aspect is how speculative such a review is. Little case law pertains. Even the language of the law(what does it really mean when we assess liability on the basis of a ‘‘trespass’’ in cyberspace?) sometimes obscures analysis.\nThis article, then, is a brief speculation on liabilities in Canada for the spread of viruses and worms.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.008 | 0.035 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.020 | 0.013 |
| Insufficient payload (model declined to judge) | 0.009 | 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".