Compromised Epistemologies: The Ethics of Historiographic Metatheatre in Tom Stoppard’s <i>Travesties</i> and <i>Arcadia</i>
Bibliographic record
Abstract
Tom Stoppard uses historiographic metatheatre to question the efficacy of historical narratives: plays such as Travesties directly address the constructed texture of history. However, partially because the 1809 scenes in Arcadia are naturalistic, critics generally accept Arcadia as presenting a “real” history. But taking anything in Stoppard’s plays at face value is a crucial mistake. Instead, we should read Arcadia as participating in a self-consciously destabilizing cultural project building a historiography of error – like Travesties, but through a less obviously constructed historiographic metatheatre – a reading that prompts us to reconsider standard narratives of Stoppard’s development as a playwright of epistemological uncertainty. Part of Stoppard’s joyous humour in Arcadia goes beyond satirizing Bernard and extends to the critical misreadings through which we, as critics, reproduce Bernard’s unreliable thesis and, like him, risk convincing ourselves that we are right. Taking Arcadia at face value undermines the ethical imperative to uncertainty and multiplicity inherent in historiographic metatheatre, an ethic that runs through both Travesties and Arcadia.
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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 | 0.012 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.070 |
| Scholarly communication | 0.018 | 0.011 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.005 | 0.010 |
| 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".