<i>Good Fences’s</i> Scripted Truths: Cultivating Dialogue in Post-Real Times
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
This article considers the presentation and performance of ‘truth’ in Downstage Theatre Company’s Good Fences. Good Fences dramatizes the relationship between the oil and gas and agriculture industries in Alberta. The show was created through interviews with ranchers, oil workers, and other Albertans, but, the creators emphasize, the final result is neither verbatim nor site-specific nor documentary, but true. Indeed, spectators of Good Fences felt the show’s strength lay in its ability to present sometimes entirely contradictory opinions: something they felt was missing in public government and media representations characterized as less than truthful. Using the idea of ‘productive insecurity’ from Ulrike Garde, Meg Mumford, and Jenn Stephenson, this article suggests that Good Fences is emblematic of a wider trend toward a kind of affective truthiness in performance that feels real and supplants the ‘really real.’ This felt truth actually serves to enact Downstage’s mandate to produce theatre that creates conversation. The show invites not the establishment of truth, but a discussion, debate, and dialogue about what several possible truths may exist. This article thus asks, what does it mean to present truth onstage, and what forms of truth are possible, or indeed desirable, in a post-fact, post-real world?
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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