“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
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it