Come to My Garbage Show!: Semiotics and Audience Development for <i>Wastelands</i>
Bibliographic record
Abstract
When working on audience development for her ecotheatre show, Wastelands, Savanna Harvey was provided the opportunity to reflect on the limitations of the word ‘theatre.’ In an analysis founded in reader-response theory, social justice, semiotics, lived experience, and digital strategy, this article challenges the contemporary linguistic utility of the word ‘theatre.’ To bring her analysis outside of theory and into practice, Harvey demonstrates how this communications gap impacts audience development. She delineates how the linguistic limitations of the word ‘theatre’ could negatively impact face-to-face interactions, marketing materials and copywriting, and digital discoverability. When producing a piece of theatre for the environment or other justice-driven causes, the urgency of the call to action should take precedence over any nostalgia or sensibility attached to the word ‘theatre.’ To this end, Harvey suggests applying a more accessible and liminal terminology, one that carries less historical baggage, bypassing barriers to audience engagement for certain audience members.
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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.009 | 0.039 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".