Bibliographic record
Abstract
Abstract This article examines the effect that cultural and technological changes have had on interpersonal communication and aims to provide an interdisciplinary explanation for the recent proliferation of defamation in electronic media. The authors argue that the absence of certain extra-linguistic cues and established cultural convention in the electronic environment often results in miscommunication which — if not itself defamatory — gives rise to emotional exchanges between interlocutors in a manner that provokes defamation. The authors begin their analysis with a discussion of defamation law as a recipient-oriented tort, demonstrating the importance of the context of communication in the determination of whether a particular remark carries a defamatory sense. In order to better understand how an online communication is received and understood by its recipients, the authors then investigate three differences between electronic and other media of communications: i) that the technology-mediated and text-bases character of electronic communication makes the process of communication more difficult and the incidence of miscommunication more likely; ii) that the nature of social interaction in the online setting has a tendency to increase hostile communications that might be considered defamatory; iii) that the cultural context and standards of communication that develop in online communities will reduce the significance of these hostile communications. Applying these considerations to the law of defamation, the authors conclude by rejecting the naive point of view that a libel published through the Internet ought to be dealt with in exactly the same way that a libel published in a newspaper is dealt with. The authors end by calling for further empirical research about the content that is produced as a consequence of contextual challenges in electronic communication.
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 | 0.008 | 0.060 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.023 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".