Bibliographic record
Abstract
Over the past three decades, the plays and productions of Theatre de la Vieille 17 have made significant contributions to French Canadian children's theatre. Their productions embrace a fantastical and imaginative narrative that make them accessible to most audiences. As much as this company shows a remarkable openness to the world, it maintains strong ties to the Franco-Ontarian theatre milieu. This thesis examines key elements that have contributed to La Vieille 17's continual commitment to children's theatre as well as ways in which its productions and policy making have resulted in its increasing artistic and financial success. This study begins by looking at La Vieille 17's three most significant plays: Le Nez, Mentire, and Meta. This analysis takes into consideration the narrative of each play, production elements, co-producers and collaborators, funding, the scope of their tour, and awards and recognition. Each of these aspects contribute to giving these productions a broader world view and help to establish La Vieille 17 as a leading producer of children's theatre. The second part of this thesis analyses key moments during the company's history as well as moments in which it has acted as a common front with other Franco-Ontarian theatre companies. Both of these activities have shaped La Vieille 17's children theatre programming and have led the company to create a successful model in which to produce their works. ll
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.236 | 0.063 |
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".