Bibliographic record
Abstract
This written portion of my thesis documents how I, as Director, set about to bring J.M. Barrie’s classic, Peter Pan to the contemporary stage. I take the reader through my in-depth research into Barrie’s many adaptations of his story, seeking an understanding of the evolution of Peter Pan and noting major elements that were retained across time and those that were changed, in search of the “true” story of Peter Pan. I explore how my discoveries informed design choices, were folded into rehearsals, and ultimately arrived on stage. In seeking the backbone of a classic, the vast interpretive history of Peter Pan and its many adaptations also gave me a sense of freedom to make my own changes. I discuss the major re-imagining of Tiger Lily and the Redskins to become the collaboratively created Never Landers, a dance ensemble of otherworldly characters sprung from the land itself. I explore the major themes I identified in the play and discuss decisions to bring darkness, longing and loneliness to the stage rather than glossing over the complex elements of the story in order to create something cute for children. Finally, I offer an exploration of the production process as a major collaboration with many artists and consider various elements of my collaborations with the design team, fight director, dance choreographer, and the actors.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.012 | 0.006 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".