Truth in the Telling: Procedure, Testimony, and the Work of Improvisation in Legal Narrative
Bibliographic record
Abstract
This article considers how legal trials participate in the production of culturally valourised narratives of truth. Trial procedures legitimate certain narratives, and certain ways of producing narrative, through procedural constraints that promulgate legal aesthetics and legal rights that articulate these procedures as norms for human behaviour. Law’s forms, then, determine law’s content, with significant connotations for the broader culture. Notable amongst these narrative forms is legal testimony, whose apparently improvised, interrogative creation imbues it with especial qualities as a representation of truth. Spontaneously performed in and effected by the valorised space of the courtroom, testimony reflects the cultural pervasiveness of law’s aesthetics, and underscores how narratives of truth are constituted by, and dependent upon, the privileged forms of their production.
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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 | 0.019 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.015 | 0.087 |
| Scholarly communication | 0.016 | 0.017 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 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".