Bibliographic record
Abstract
Online Civil DisobedienceO nline civil disobedience is the use of any technology that con- nects to a network in pursuit of a cause or a political or social end.There are many forms of online civil disobedience.A person or groups of individuals may block access to a website, redirect web traffic to a spoof website, deface a website, or flash messages on screen.The off-line equivalents would be a sit-in blocking access to a building, a protest that prevents people from using a street such that they are redirected, protesting with signs and images, or handing out flyers or placing flyers in mailboxes.Some of these off-line activities are illegal while others are not.As will be seen, some of the equivalent off-line acts are legal while the online equivalent is ambiguous at best, and at worst will attract civil liability or criminal sanction.It is important to reiterate the difference between online civil disobedience and hacktivism.Because hacktivism (as discussed in ch. 5) involves the unauthorized access and/or use of and/or interference with data or computer or network, it always falls within the purview of a crime.This is because the so-called Budapest Convention-the only institutional arrangement for international cooperation on cybercrime-makes unauthorized access, use, or interference of data, a network, or a computer illegal.There are no exceptions for security research or public interest found in the convention.Many countries, including Canada, Australia, and those of the Europe Union, are signatories to the convention and, as such,
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.140 | 0.044 |
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".