Bibliographic record
Abstract
:This essay takes, as a focus, Sherlock (2010–) and London Spy (2015) to argue for the importance of faith in contemporary British television. It reveals how the two series characteristically tax their central focalizers' (and the viewers') trust and advocate the value of taking a leap of faith—in essence, a (re)turn to trust. With close attention to form, imagery, and language, this study reveals how Sherlock uses John's (Martin Freeman) trust of Sherlock (Benedict Cumberbatch) to explore the processes by which information is variously produced and disseminated: I trace how the conflict between Sherlock and Moriarty (Andrew Scott) operates as a structuring principle, and how this is destabilized, in “The Reichenbach Fall,” when Moriarty offers a plausible counter-narrative, presenting himself as an actor and Sherlock as his employer. If initially suspicious, John believes in Sherlock despite the overwhelming evidence leveled against him. I go on to examine how, in a similar rhetorical move, Danny (Ben Whishaw), the central protagonist in London Spy, grows from being a victim to the unwilling investigator of his lover Alex's (Edward Holcroft) murder despite the accounts offered by the media. My article illustrates how Sherlock and London Spy shift our attention from global issues to personal stories, how the truth offers neither liberation nor solace in both series, and how faith ultimately brings together their characters. In so doing, I demonstrate how the two series variously recall and raise questions about Conan Doyle's stories.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.034 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 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".