Bibliographic record
Abstract
Through the following pages I will catalogue my directorial process of researching, rehearsing and reflecting on my thesis production of Les Belles-soeurs by Michel Tremblay. The production ran at the Frederic Wood Theatre from March 16 to April 1, 2017 and was a critical and popular success. Les Belles-soeurs utilized the talents of fifteen women actors from the UBC theatre program, as well as student designers, production personnel and crew. This written thesis will attempt to illustrate my creative choices, script analysis, and staging ideas in relation to my research, and in collaboration with the other artists and design team. An effort will be made to highlight what worked well, what changed, and what perhaps should have changed. There are also notes from my Director’s Diary beginning at the start of my Masters work. These notes chronicle, over two years, my learning curve here at UBC and are included because they informed the process of my thesis production. In addition to a detailed analysis of my process with Les Belles-soeurs, therefore, there is also some analysis of other plays that led up to my thesis production. All of this informed my work on Les Belles-soeurs and are reflected upon within this document as part of my Masters work.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.022 | 0.005 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.024 | 0.004 |
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".