I wish I was a Jellyfish: Reflections on Artistic Leadership and Directing Jawbone
Bibliographic record
Abstract
This artist statement accompanies the Fall 2020 production of Meghan Greeley’s Jawbone at the University of Calgary’s University Theatre. The show was produced by the University of Calgary Drama Division in the School of Creative and Performing Arts and was supervised by Christine Brubaker. This document outlines the creative process of Jawbone as well as the development of my directorial and leadership skills. Chapter One outlines how I was drawn to Jawbone, as well as the artistic questions that I was pursuing inside of it. The second chapter documents my research leading up to the creative process of Jawbone, including interviews that were conducted with artistic leaders across Canada, as well as the artistic core values that I held before entering into the Jawbone process. In Chapter Three, I focus on the conceptual journey and the resulting design process for Jawbone. This chapter examines the skills that I gained throughout the design process and interrogates whether my pre-existing core values served me or not. In Chapter Four, I discuss the process from auditioning up until opening night. This chapter explores working with the actor, directorial challenges and resulting discoveries. Chapters Five and Six outline my major takeaways and learnings from the process overall. In Chapter Six, I discuss how my core artistic values have changed and developed.
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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 | 0.013 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.031 | 0.029 |
| Scholarly communication | 0.014 | 0.005 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.004 | 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".