‘Just a singing-machine’: The Making of an Automaton in George du Maurier's <i>Trilby</i>
Bibliographic record
Abstract
This essay argues for a re-evaluation of the eponymous heroine of George du Maurier's 1894 bestselling novel, Trilby. Trilby's tragic end is generally understood to come at the hands of that archetypally evil impresario, Svengali, who purportedly mesmerizes and manipulates her into becoming Europe's greatest singing star. However, a closer examination of her life reveals that Trilby's fate in the novel can more properly be linked to a lifelong dehumanization that shatters her sense of autonomous self and reduces her to a most rudimentary version of the human. In her progression from aspiring subject to tractable ‘singing-machine,’ Trilby can, in fact, be positioned as belonging to the cultural genealogy of the automaton, a figure that symbolizes a particular nineteenth-century concern about the fate of human subjectivity in an increasingly rationalized, systematized world.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.025 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| 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".