Bibliographic record
Abstract
New technologies challenge the law in many ways, including by extending a person’s capacity both to harm others and to defend him or herself against harm from others. These changes require the law to decide whether we have legal rights to be free of those harms, and whether we may react against those harms extra-judicially through some form of self-help (e.g. self-defense or defense of third parties) or whether we must resort to legal mechanisms only. These questions have been challenging to answer in the cyberspace context, where new interests and new harms have emerged. The legal limits on permissible self-defense have historically been a function of necessity and proportionality to the threat. However, this article argues that the case law and historical commentary show that equality between individuals is an important policy issue that underlies the limits on self-defense. The use of technologies in self-defense brings the question of equality to the fore since technologies may sometimes neutralize an inequality in strength between an attacker and a defender. A legal approach that would limit resort to technological tools in self-defense would ratify and preserve that inequality. However, the relationship between technology and human equality is complex, and this article proposes an analytical structure for understanding it. The objective is to understand which technologies promote equality while imposing the least social costs when used in self-defense. The article proposes principles (including explicit consideration of the effects on equality) for setting limits on technological self-help, and illustrates their use by applying them to several forms of cyberspace counter-strikes against hackers, phishers, spammers and peer-to-peer networks.
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 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".