Bibliographic record
Abstract
Are you doing a play by Tennessee Williams? Or one of David Mamet’s plays set in Chicago? Need to learn a Southern or Boston or New York or Caribbean Islands accent quickly, or do you have plenty of time? Then Teach Yourself Accents – North America: A Handbook for Young Actors and Speakers is for you: an easy-to-use manual full of clear, cogent advice and fascinating information. Contemporary monologues and scenes for two are included, and an enclosed CD contains the extensive practice exercises. Perfect for the young acting student, the book will help anyone beginning a study of accents to get a rapid handle on the subject and use any accent immediately, with an authentic sound. More experienced actors who need an authoritative quick guide for an audition or for role preparation will find it equally useful, as will speakers who want to improve a specific accent or liven up a presentation with an apt anecdote. This second volume of the new Teach Yourself Accents series by Robert Blumenfeld, author of the best-selling Accents: A Manual for Actors, covers General American, the most widely used accent of Standard American English, as well as Northern and Southern regional accents, AAVE (African-American Vernacular English), Hispanic, Caribbean Islands, and Canadian English and French accents.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.092 | 0.043 |
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".