The production format and ambiguity of principal in improvised entertainment interaction
Bibliographic record
Abstract
This investigation utilizes interactional sociolinguistics to consider the role of principal in fictionalized improvised entertainment. We explore how improvised contexts can offer an ambiguity of principalship. The animator could in fact be the principal, offering the performer the potential to articulate cultural truths. The possibility that he/she may not be provides a freedom from responsibility for what is being said, a space that performers may exploit to animate controversial beliefs without the normal interactional repercussions. We explore this in two sets of data, one from a production day of a Canadian television series in which the dialogue is improvised, and one from a large-scale ethnography of a community in Washington, DC, of performers of improv (a humorous theatrical performance in which characters, dialogue, and the performance structure itself are improvised). Observing footing shifts and drawing on metacommentary, we consider how improvisers show awareness of this potential, and we consider the cultural implications. Thus, while improvisation provides a unique forum for tackling difficult cultural issues, such as racism, improvisers may avoid doing so precisely because of the ambiguity of principalship. This research contributes to the current work in sociolinguistics considering the complex ways that language may be used in interaction to articulate cultural meaning.
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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 | 0.006 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".