The late-period Sherlock Holmes: Nick Lane and Luke Barton on adapting The Valley of Fear
Bibliographic record
Abstract
In this two-part interview, I discuss Blackeyed Theatre’s production of The Valley of Fear , an adaptation of Arthur Conan Doyle’s 1915 Sherlock Holmes novel, with writer-director Nick Lane and actor Luke Barton. In the first half, I go over, with Lane, some of the changes that he makes to the novel’s form to put Holmes on centre stage, his tantalization of audience members with ciphers, and his emphasis on the detective’s and his biographer-cum-sidekick Dr Watson’s friendship, on which so much relies. In the second half, I discuss, with Barton, his return to Holmes and how he has kept each of his performances of the detective distinct. We explore the detective’s thought processes and how he displays them on stage and, for the recording, on camera; his limited but meaningful engagements with Moriarty, whose appearances in the Holmes canon are few and far in between; and finally, the decisions that the detective makes in relation to Birdy Edwards’s case. This conversation is a sequel to our earlier one (Ue 2021) and it advances scholarship by celebrating Blackeyed Theatre’s significant contributions to Holmes’s afterlife. It shows the insight and care that is put into each adaptation; it examines Lane’s and Barton’s creative decisions in yet another fine project; and it reveals how a novel, one which has never ranked amongst Conan Doyle’s finest, can profit from a second life on stage.
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How this classification was reachedexpand
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".