"Playing the Men": Ellen Tree, Fanny Kemble, and Theatrical Constructions of Gender
Bibliographic record
Abstract
Ellen Tree, the first English performer to regularly play tragic male roles, initiated a nineteenth-century Anglo-American convention in which many women performers played a limited number of tragic male roles, primarily Romeo and Hamlet. Nineteenth-century women's performances of tragic male characters point to multiple tensions and fissures in the understanding and representation of gender in theater. Tree's negotiation of these tensions and her decisions about how, and in what contexts, to play tragic male roles indicate her awareness of the ways in which shifting social perspectives on gender might be accommodated in her stage representations. She followed her enthusiastic and romantic Romeo with Thomas Noon Talfourd's Ion, whose dignified and "classical" character offered a kind of idealized and unsexualized masculinity that was not too deceptively "realistic." Thirty years after she had first performed Romeo with Fanny Kemble as Juliet, Tree attended several of Kemble's public readings of Shakespeare, in which Kemble read all the roles. Tree expressed a combination of admiration and disquiet at the way Kemble "read the men best." Tree's mixed emotions about Kemble's reading register not only her recognition of changing social mores, but also unresolved tensions and ironies in Tree's own theatrical practices. Audience responses to Kemble's readings show astonishment at her intensity and emotionality, particularly her strikingly convincing readings of male characters. These strong audience reactions to Kemble's readings of Shakespeare's plays suggest that the expressions of emotionality possible in public reading could be perceived as more dangerously exciting than stylized and conventional stage performances of the period. Kemble had often criticized acting as overly emotional, and considered stage performance to be less respectable and less intellectual than reading plays aloud without costumes or stage action. Yet Tree, who had performed regularly onstage for over forty years, was shocked by the emotion and lack of restraint in Fanny Kemble's reading. The contradictions and ironies in responses to the stage performances and readings of Tree and Kemble indicate they were both judged for exceeding social expectations of womanly restraint, even as they felt, expressed, and responded to strong emotion in both reading and performance.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.012 | 0.012 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".