Converting time reference in judges’ summations: a study in time reference management in a Creole continuum courtroom
Bibliographic record
Abstract
When witnesses take the stand in court, they attempt, for the most part, to reduce the past experience of a crime to a story. This story is usually co-created and mediated by a lawyer via examination in chief or cross examination. What can potentially emerge as a result is a series of competing narratives – different, and sometimes contradictory, versions of the same story. Judges must somehow find a way to consolidate all the competing narratives inside the courtroom before arriving at the verdict, or, in juried cases, instruct the jury on how to arrive at a final decision. This article examines the techniques the judge uses to consolidate one particular detail - time. Since the linguistic situation in Jamaica is described as a Creole continuum moving between Jamaican Creole (JC) and Standard Jamaican English (SJE), judges have the complex task of navigating markedly distinct ways of representing time both lexically and grammatically. The study explains the tense conversion technique which judges in the Jamaican courtroom use when moving between the TMA (tense mood aspect) systems of JC (input during the trial) and SJE (output during the summation). The study reveals that some tense conversions in the summation may in fact be contrary to what was the intended meaning during the testimony and as such pose a problem for interpretation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".