Bibliographic record
Abstract
This piece was created in Orchesis during fall quarter and will be a part of the annual Orchesis end-of-the-year dance performance. This piece began as a series of movements that I gradually put together to create a long unified movement. I found the song “Breathe Me” by Sia and decided to use this for my choreography because the music accented the movements perfectly. I originally planned on having six dancers but came to a final decision of eight. An even number was essential for this piece because the dance is based on finding yourself and fighting between who you are and who you want to be. The pathway to finding oneself can be difficult and can sometimes lead you in many directions, which is why I decided to name this piece Dualities. Throughout this dance, there is a lot of partnering or movements that are soft and fluid while other movements are fast and strong to show the conflicting feelings people go through when finding their way through life. The dance starts off slow and peaceful and gradually becomes more chaotic and conflicting. Toward the end the dance, the movements slow down again as some dancers run or slowly walk off stage to show different ways of coping with change within oneself. The dance ends with the last two dancers facing each other to show that it is possible to overcome obstacles and find your true self.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.009 |
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".