After<i>The Contrast</i>: Tyler, civic virtue, and the Boston stage
Bibliographic record
Abstract
B y 1825, theatre was well ensconced in the united States. Having spread to all major seaboard cities, it was poised to move westward, and in fact had done so in part already. Cincinnati, New Orleans, Mobile, and St. Louis were but a few of the new theatre centers that would become important stops on actors' tours in the next two decades. At the end of the century's first quarter, most of the generation of actors who had brought the stage back to the former colonies after the war had retired or died; before long, American-born actors, such as Edwin Forrest and Charlotte Cushman, would rival and ultimately supplant the British stars who would continue to seek fame and gold in the New World. The success of such plays as Woodworth's The Forest Rose made clear to managers that American-written and set material could be lucrative and that audiences would demand more in the way of American types: the stage Yankee to be sure, but also American-honed versions of British dramatic stock characters, most especially the stage African and stage Irish. Democratic energies infused what some had tried to forge as an elite-dominated entertainment medium, and as theatres got larger, holding two or three thousand patrons, spectator demographics more accurately reflected the population as a whole. If anything, 1825 would have been a good time to look back and to look forward, to see where theatre had come and where it would go.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.007 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.041 | 0.006 |
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".