<i>Good Fences’s</i> Scripted Truths: Cultivating Dialogue in Post-Real Times
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?
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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 | 0.015 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.011 | 0.043 |
| Scholarly communication | 0.012 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".