Omniscient Narrative Modes in Law: From Trial Strategy to the Fellow-Servant Rule
Bibliographic record
Abstract
Research in law and literature often uses the term “narrative” as a shorthand for various kinds of motivated legal reasoning, indicating that facts, doctrines, and the relations among them have been chosen and arranged for a particular purpose. Alternatively, speaking of “narrative” may be a way of conveying that one is concerned with interpretation, and may be a signal that the discussion will focus on images, symbols, representations, or ideologies, even if their narrative features play little or no role in the analysis. This article shows how research on narrative might help to clarify aspects of trial strategy and legal doctrine. The first section considers omniscient narration as a way of understanding the effects of various defense strategies, in a criminal trial. The second section considers the role of omniscient narration in the development of the “fellow- servant” rule in the nineteenth century. The law of evidence provides an especially fruitful area for such investigations, but questions of narrative form and technique can help to clarify many other aspects of forensic argumentation and analysis, in both procedural and substantive contexts.
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 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.039 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".