Bibliographic record
Abstract
Working from the premise that theatre and performance can yield valuable insights about the operations of race, this article explores the dramaturgical strategies Lydia R. Diamond deploys in her 2014 comedy, Smart People, to interrogate the complexities of racial politics in the twenty-first century. I trace how, through the intertwined narratives it weaves for its four protagonists, Smart People engages important debates about the rebiologization of race, the psychic costs of stereotyping, and the vexed representational politics of US theatre, thereby bringing into sharp relief the ways in which narratives of racial progress obscure the material ramifications of race in contemporary life. Even as it trades in the signs of progress the United States has made on race matters, Smart People illustrates for its audiences why a proper reckoning with racial formations, ideologies, attitudes, practices, and beliefs remains as urgently needed as ever. In so doing, the play participates in and extends a long tradition within black expressive culture of using theatre and performance to provoke social action and 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.034 |
| Scholarly communication | 0.011 | 0.006 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".