Bibliographic record
Abstract
This report from the field describes some of the author’s methods of audience engagement as a means of social engagement, discussing the implications for practice. The report invites dialogue with the reader about the usefulness of audience engagement and ways it can be manifested before, during and after performance. Theatre is a vibrant and valuable tool for sparking dialogue and inspiring action around challenging social topics. Audiences who are engaged in the process of the performance beyond the standard role of passive spectator are more likely to be motivated to deliverable endeavors post performance. This report from the field offers four brief case studies as examples of audience engagement and includes pragmatic techniques for using theatre as a vehicle for personal and social change through audience engagement. It explores how artists can galvanize and empower audiences by creating experiential communities pre, during, and post-show. Drawing upon examples from high-quality international theatre projects written and directed by the author, the essay investigates and describes the work of The H.E.A.T. Collective including My Heart is in the East (U.S., U.K. and Europe), The FEAR Project (produced in the US, India and Czech Republic), Emma Goldman Day (U.S.).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.011 | 0.022 |
| Scholarly communication | 0.017 | 0.009 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.016 | 0.002 |
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".