Bibliographic record
Abstract
Hansel und Gretel is a well-known fairy tale opera by the late romantic composer, Engelbert Humperdinck. It was originally sung in German but for this scene it will be sung in English. Our scene takes place deep in the woods just before dusk. Hansel and Gretel are searching for strawberries when they realize they have lost their way home. The sandman comes to come calm their fears and sprinkles magical sand in their eyes to help them fall asleep. They end the scene singing a prayer and sleep in each other’s arms. Hansel und Gretel is a great example of late romantic German opera. Many German operas of this genre had elements of magic, a wide variety of characters, were set in the outdoors, and were often based on fairytale, myth, or legend. Humperdinck was a student of Richard Wagner; he used his teacher’s works as inspiration. This can be heard in the chromatic and dramatic writing in Hansel und Gretel. The preparation for this scene started in December when we received our music. We were given all of winter quarter to learn our parts and were expected to have it memorized at the beginning of spring quarter. Then within the course of about seven weeks we worked on staging and refining our characters. The week before the show performances we rehearse intently making sure the lighting and all details were performance ready for the performances in Hertz Hall.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.092 | 0.053 |
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".