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Record W4400880193 · doi:10.1386/jafp_00111_7

The late-period Sherlock Holmes: Nick Lane and Luke Barton on adapting The Valley of Fear

2024· article· en· W4400880193 on OpenAlexaff
Tom Ue

Bibliographic record

VenueJournal of Adaptation in Film & Performance · 2024
Typearticle
Languageen
FieldArts and Humanities
TopicCrime and Detective Fiction Studies
Canadian institutionsCape Breton University
Fundersnot available
KeywordsPeriod (music)ArtPeriod lengthHistoryArchaeologyAestheticsMathematics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.754
Threshold uncertainty score0.276

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.037
GPT teacher head0.248
Teacher spread0.210 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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