A transitivity-based exploration of a wrongful conviction for arson and murder
Bibliographic record
Abstract
Abstract Well-known cases of wrongful convictions (e.g. Central Park Five, Steven Avery, Amanda Knox), although merely the tip of the iceberg, serve to highlight flaws inherent in justice systems worldwide (cf. Garrett 2011 ). Many innocent people are having their freedom taken away without reason. One such lesser-known, though very significant, case is that of Kristine Bunch, who was wrongfully convicted of arson and murdering her son, resulting in her wrongful imprisonment for 17 years. To examine how Kristine represents her miscarriage of justice discursively, I examine transitivity patterns ( Halliday & Matthiessen 2014 ) in a semi-structured interview with her and, in doing so, aim to create awareness of some probable key language processes in wrongful convictions more generally.
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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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.002 |
| Science and technology studies | 0.008 | 0.020 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 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".