Bibliographic record
Abstract
Whereas eligibility for copyright protection requires originality, that criterion is not normally applied to judicial opinions. Like other forms of legal prose, judgments are collaborative products that reflect a wide range of imitative writing practices, including quotation, paraphrase, and pastiche. Yet the definition of originality in copyright law has important commonalities with the generic expectations associated with judicial decisions. One way in which judges show that they have considered all sides of a dispute is to explain the outcome by means of an independently produced rationale. Precisely because judicial prose typically includes a significant amount of copying, however, it is doubtful that any requirement concerning original prose is desirable or could be consistently applied. To explore that issue, this article considers the least demanding standard that might plausibly satisfy the parties – namely, a standard demanding that judges display their own skill and judgment in every part of the judgment that may determine the outcome. This requirement, it turns out, would be difficult to apply and would promote meritless appeals. The analysis shows why judicial copying is different from plagiarism, and this distinction sheds light on recent disputes over various forms of copying in trial judgments, involving copying from the pleadings (with or without attribution) and unattributed copying from law journal articles or from other judgments by the same judge or by others.
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 | 0.021 | 0.078 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.006 | 0.038 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".