Bibliographic record
Abstract
In Casting Directors' Secrets, casting directors from New York, Los Angeles, Toronto, and Vancouver offer insight in their own words into the do’s and don’ts of the audition process and reveal the three biggest mistakes made by actors at this crucial stage. The book offers instruction in these areas and more: How to get the audition – training and preparation, headshots and resume, finding an agent, auditioning for agents; audition/interview etiquette – being late, canceling your appointment, waiting room do’s and don’ts, staying focused, filling out the paperwork, behavior toward other actors; bad habits – perfume and cologne, first impressions, don’t look for the casting couch!, you and your ego, brown-nosing, tell the truth but not the whole truth; artistic preparation – what your agent should tell you, working with sides, eye contact and the fourth wall, ice-cold readings; performing the audition – rewriting the dialogue, false starts, losing your place, violence in audition scenes (don’t make it too real!); growing as an actor – taking risks, attending classes, maintaining the momentum; and more!
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.008 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.099 | 0.029 |
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".