“Well, now, you asked them. Does that mean that they were expected to go?’: Master Narratives and Counter-Narratives in the Trial of Adnan Syed
Bibliographic record
Abstract
A criminal trial in a traditional Western adversarial justice system is performed as a discursive battle of competing narratives between prosecution and defence. In the end, decisions by the judge and jury, while ostensibly premised on the strength of the evidence, rely in large part on the relative persuasive strength of the two stories – which one is more plausible? Commonsensical? Familiar? After exploring the positioning of narrative studies within the field of Criminology, this article will draw on ethnomethodology, talk-in-interaction, and narrative analysis to examine a trial that took place in the United States in 2000 – that of Adnan Syed. In order to appeal to cultural understandings shared by the American jury, trope stories were deployed by both sides. Prosecution told the story of Adnan Syed, a Jilted Muslim Lover, defending his honour after the victim broke up with him. Meanwhile, defence countered with a Star-Crossed-Lovers narrative, in which there was no motive for violence. I will argue that defence failed to deploy their story effectively and, in their attempts to counter the prosecution’s narrative, rather ended up reinforcing its terms. The triumph of the prosecution’s case may be found in the details of how the defence’s counter-narrative failed.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.019 | 0.027 |
| Scholarly communication | 0.011 | 0.012 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.004 | 0.009 |
| 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".