“The Jogger and the Wolfpack”: An Analysis of the TRANSITIVITY Patterns in the Global Media Coverage of the 1989 Central Park Five Case
Bibliographic record
Abstract
Abstract Common causes of wrongful conviction include eyewitness misidentification, improper forensics, or false confessions (Garrett in Convicting the innocent: where criminal prosecutions go wrong, Harvard University Press, Cambridge, 2011; Innocence Canada, https://www.innocencecanada.com/causes-of-wrongful-convictions/ ); whilst none of these factors are in question in this paper, the notion put forward is that a more implicit factor is also at play; that is, the newspaper coverage of a criminal case during the lead up to trial. According to Felton Rosulek (Text Talk 28:529–550, 2008), “[…] linguistic choices conspire together […] and create a specific interpretation of reality”. Thus, this paper explores how the accused and the (alleged) criminal events pertaining to a high-profile case of the 1980s in New York are discursively framed in a range of press coverage across the USA and further afield. The corpus comprises newspaper articles reporting on the Central Park Jogger case, which resulted in the wrongful conviction and lost freedom of five innocent young men. Using corpus-assisted critical discourse analysis (CADS) (Partington and Marchi, in: Biber, Reppen (eds) The Cambridge handbook of English corpus linguistics, Cambridge University Press, 2015; Stubbs in Text and corpus analysis. Computer-assisted studies of language and culture. Blackwell, Oxford, 1996), the Transitivity patterns (Halliday and Matthiessen in Halliday’s introduction to functional grammar, Routledge, London, 2014) present in the press coverage are examined to gain insights into whether the portrayal of (1) the accused and (2) the victim at the centre of this case may have contributed to securing a wrongful conviction. Furthermore, this paper strives to (1) draw awareness to wrongful convictions more generally and (2) contribute to studies on Transitivity, which serve to highlight societal injustice and the power of printed news when determining the innocence or guilt of an accused individual. To acquire both quantitative and qualitative results, the UAM Corpus Tool (O’Donnell in UAM Corpus Tool, http://www.corpustool.com/ ) was also employed here.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".