Bibliographic record
Abstract
If an individual or company is defamed online, they have two options to resolve the dispute, absent a technical solution. They can complain to an intermediary or launch a civil action. Both are deficient for a variety of reasons. Civil litigation is often unsuitable given the nature of online communications (across different platforms, jurisdictions, involving multiple parties, and spread with ease), the length and cost of litigation, and the ineffectiveness of traditional remedies. Intermediary dispute resolution processes can sometimes be effective, but lack industry standards and due process, place intermediaries in pseudo-judicial roles, and depend on the changeable commitments of management. At its core, the problem is the high-volume, low-value, and legally complex matrix of online defamation disputes. In this article, I ask: Are there alternative ways to resolve disputes that would improve access to justice and resolution for complainants? The key to resolving some of these problems, I argue, is revisiting the basic issue of what complainants want in the resolution of a defamation dispute and then connecting this with innovations in dispute resolution. Ultimately, I recommend the creation of an online tribunal as a complement to traditional court action. In coming to this conclusion, I explore various issues and proposals for reform, including the challenges wrought by online defamation, what defamation claimants want when they sue, the role of technology in resolving such disputes, streamlined court processes, online dispute resolution, and the regulatory role of intermediaries.
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 | 0.031 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.012 | 0.038 |
| Scholarly communication | 0.035 | 0.053 |
| Open science | 0.005 | 0.019 |
| Research integrity | 0.017 | 0.018 |
| Insufficient payload (model declined to judge) | 0.018 | 0.004 |
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".