Bibliographic record
Abstract
Abstract Colin Kaepernick takes a knee during the singing of the national anthem at an NFL game, and the digital midwife helps birth a movement. Mike Pence is called out from the stage at a performance of the smash-hit musical, Hamilton, and the President of the United States takes to Twitter in rebuttal. New York’s Public Theater and its acclaimed artistic director, Oskar Eustis, stage a thinly veiled parable of the Trump presidency in their Shakespeare-in-the-Park production of Julius Caesar. Images of the performance are alternatively venerated and eviscerated on social media, frightened sponsors pull out and audiences attack the stage. Welcome to the new arena, where the theatre and the stadium have once more taken their places as flashpoints for political protest, and where digital media are not mere witnesses, but powerful participants. This article deploys these examples to ‘re-image-ine’ classical notions of the epideictic as rhetorical display. Engaging with William Beale’s 1978 rhetorical performative update to this classification, along with theorizations of performance and technical images, I argue the epideictic has the unique ability to reflect the values of the community in the moment, activating audiences and speakers in new ways. Modern audiences, informed by digital technologies and evolving relationships to the live event, have entered new areas of interaction and performance that invite new scholarship and explication. How can epideictic performance, (conceived as display and ceremony, but also as live in ways that deliberative and forensic rhetoric are not) act as a useful theory for understanding digital realities and moments of disruption and resistance?
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.010 | 0.045 |
| Scholarly communication | 0.017 | 0.015 |
| Open science | 0.001 | 0.017 |
| Research integrity | 0.003 | 0.005 |
| 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".