Bibliographic record
Abstract
Abstract: A conversation between an emerging theatre artist (Mariah Horner) and a Queen’s University professor (Grahame Renyk), this article analyzes Stones in the Woods, a production mounted in 2014 by Mariah’s historical and site-specific company, the Cellar Door Project. The production was performed around a stone in a park that was originally a part of Kingston’s nineteenth-century observatory. Drawing inspiration from thing theory (as discussed in Performing Objects and Theatrical Things, edited by Marlis Schweitzer and Joanne Zerdy) and Pierre Nora’s work on lieux de mémoire, Mariah and Grahame discuss how the performance treats the stone as an actant, equal in status to the human actors. By foregrounding the stone in its “thingness,” the performance highlights its material longevity and reveals its presence in actor networks past, present, and since. Clear threads are tied from the past, through the stories in between, and into the present, reconnecting audiences with a sense of historical continuum at the site. Distinguishing the Cellar Door Project’s work as a rematerialization rather than a reanimation of history, Mariah and Grahame discover that this kind of work may be a more effective approach than re-enactment for connecting ourselves with those who came before.
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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.007 | 0.018 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| 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".