Viewpoints/Points of View: Building a Transdisciplinary Data Theatre Collaboration in Six Scenes
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Data now plays a central role in civic life and community practices. This has created a pressing need for new forms of translation and sense-making that can engage diverse publics. Research-based Theatre (RbT) has proven to be an effective approach to delivering qualitative data to community stakeholders. We extend this tradition by proposing “community-engaged data theatre”. This approach translates quantitative data into theatrical language to engage communities in deliberative conversations on relevant issues. Community-engaged data theatre requires bridging multiple disciplines and involves creating new definitions and shared vocabularies in discourses that formerly have had little overlap in meaning. In this article, we share key insights from our initial experiments in which we adapted quantitative and qualitative data to devise a pilot piece in collaboration with a local community partner. In this essay, we communicate our collaborative process in polyvocal, artistic form. We edit and adapt materials from our conversations and creative practices into scenes illustrating how we taught and learned from each other about data science, participatory modeling, material deliberation and Composition to pilot our lab’s first community-engaged data theatre prototype.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.006 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it