Bibliographic record
Abstract
This essay challenges the tendency in censorship studies to focus on banned works and causes célèbres. It suggests that critics need to attend more carefully to adversarial works that have eluded detection by authorities to better understand censor-avoiding and censor-baiting techniques. In examining Bernard Shaw's The Devil's Disciple, I argue that Shaw was subversive of morality and censorship through encoded attacks on the institution of the Lord Chamberlain's Office. This becomes clearer through analysis of Shaw's censorship discourse in the years following the writing of the play in which he taps into its ethos and adopts the perspective of the title character, especially in his promotion of American-style post-performance trials of plays as against the British system of applying for licences before production. Shaw also undertook auto-intertextuality by drawing upon The Devil's Disciple in composing The Shewing-Up of Blanco Posnet, a play that intentionally provoked British censors and provided him with ammunition when he appeared before the Joint Select Committee on the Censorship of Plays in the summer of 1909. I end by noting how Shaw himself refused war-time productions of The Devil's Disciple because of its threats to morality and its potential for instigating radical change.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.017 | 0.041 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".