Mom as Community: A Conversation on Aging in Tara Beagan’s <i>Honour Beat</i>
Bibliographic record
Abstract
Dans cette contribution, Lindsay Lachance s’entretient avec Tara Beagan, lauréate du prix Siminovitch en 2020, au sujet des personnages féminins de sa pièce Honour Beat. Au fil de la conversation, nous apprenons comment la famille de Beagan et leurs expériences intergénérationnelles figurent dans l’univers de sa pièce. La dramaturge interroge les notions de jeunesse, de joie et de souvenirs incarnés en demandant que la plus jeune des comédiennes interprète le rôle du personnage le plus âgé. Honour Beat est un univers dans lequel la mère sert de communauté et de chez soi, et elle nous invite tous à prendre le thé chez elle.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.061 | 0.018 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.006 | 0.014 |
| Insufficient payload (model declined to judge) | 0.007 | 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".