Bibliographic record
Abstract
Abstract In this article, I analyze a rhythmic device in musical theater performance that is often used but rarely discussed: The sudden break into halftime. Breaking into halftime—a rhythmic shift that is performed and perceived as occurring at half the tempo (or speed) of the groove in preceding and subsequent sections—has the effect of abruptly stretching out and slowing down musical temporality. With a break into halftime, a song's groove is suddenly broader, more open, and expansive. When accompanied by lyrics, the move suggests deliberateness, calls attention to the lyrical address, and invites a considered response from the listener. It is a musical device akin to drastically slowing down a spoken cadence to ensure that the listener is fully grasping the significance of one's words. With or without lyrics, this temporal disruption has a powerful effect on listeners’ bodies as, suddenly, the established timing of one's musical experience is disrupted and immediately set to half the speed of the previous pace. By analyzing the use of halftime in three filmed musicals—Newsies, Rent, and the Rocky Horror Picture Show—I show how the rhythmic device carries with it social coding of solidarity and collective power among queer, marginalized, and/or otherwise outcast individuals and ultimately alters the broader narrative of the shows. Ultimately, I articulate how halftime serves to queer time and forge collective action, allowing us to reimagine what sort of politics, formations, collectives, and futures are possible.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.008 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".