Cheering for a Change: Actor-Cheerleaders Revive an Ancient Choral Tradition
Bibliographic record
Abstract
Every day across Canada, legions of underemployed actors gather at local pubs to pound out their frustrations about not having a gig and elaborate on what they would do to change their situation if only they had the opportunity to do so. Sadly, many of those passionate ideas and dreams never make it past the bottom of the pint. Maybe it has something to do with the fact that actors, infinitely creative and imaginative people, are classically conditioned to do ONLY what they are told, when they are told and in the costume they are told to wear. It is a culture of limitation. We often sigh and proclaim, “Such is the nature of the beast.” But does it really have to be this way? What if we all found new ways to realize our dreams for the theatre? Sometimes you just gotta stand up and try to change the world you live in. This is a story of a group of Montreal actors who are doing just that. We are here to say, “It can be done!” Using the POWER OF CHEER, we are creating our own form of personal artistic expression. In the process, we have uncovered a connection between cheerleading and theatre deeper than anyone had ever imagined . . .
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 | 0.009 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.016 | 0.025 |
| Scholarly communication | 0.013 | 0.005 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".