“‘Love all, trust few, do wrong to none. If only that were true… Is that why you keep us all so close?": Detective Inspector River’s inner victorian poisoner
Bibliographic record
Abstract
On Tuesday, 13 October 2015, BBC One broadcast the first of six one hour installments of the groundbreaking detective drama miniseries River, written by Abi Morgan. I refer to River as ‘groundbreaking’ not in reference to the critical reception and a review of its titular character DI John River (Stellan Skarsgård), but in appreciation of a drama that was saturated in a rich concept, was created in an impossibly intricate production process, involved the highest echelon of committed research and performance-centred talent, and offers inordinate academic possibilities in terms of critical interpretation. For the purposes of this paper, interpretation will rest on the clear and present ways in which both the public and private aspects of our ‘anti-hero’, John River, pursues detection and rectifies criminality while practicing evasion in the detection of his disability. River offers a clear meta-narrative of crime, mental health drama; neo-gothic ghosting; psycho-social commentary; and Victorian true crime history on film while providing a collapsed survey of the detective, then and now, in a contemporary crime fiction that relies on a Victorian serial killer as foil. You see, John River is never alone on the beat or in his head because he hears voices. Voices of dead people. Oh…he sees and interacts with them as well. In public. Persistently. But only one goads him; only one angers him; only one does he fear, that of Thomas Cream (Eddie Marsan). All of his Revenants are recent deaths that occurred on his watch, centrally that of his beloved partner DI Jackie “Stevie” Stevenson (Nicola Walker), who are with him for a time until they outlive their use. All except Cream – a true-life crime character from history. Thomas Neill Cream (1850 – 1892) was a Glaswegian-Canadian doctor, abortionist and serial killer who poisoned countless women internationally. Cream was arrested, following surveillance, for the murder of Matilda Clover in South London in July of 1892 and hung shortly there-after. River is reading a book on him at the start of the series, and he invades his consciousness in all the wrong ways. This paper critically interrogates the Cream and River relationship in the context of captivation, crime fiction, and psychic need.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.011 | 0.006 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.013 | 0.003 |
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".