Bibliographic record
Abstract
Irma H. Collins. Dictionary of Music Education Lanham, Maryland: The Scarecrow Press, Inc., 2013, 338 pp. ISBN 978-0-8108-8651-3 (cloth), 978-0-8108-8652-0 (eBook) Irma H. Collins has had a long career teaching in public schools and universities in the United States and is currently professor emeritus of music education at Murray State University in Kentucky. She served as the national chairperson for the National Association for Music Education (NAfME) Society for Music Teacher Education and founded the Journal of Music Teacher Education. Collins received a DMA degree in music education from Temple University, an MM in voice at Peabody College in Nashville, Tennessee, and a BA in violin from Ouachita Baptist University in Arkadelphia, Arkansas. The Dictionary of Music Education is a practical source of information on music education in English-speaking countries, including the United States, Australia, New Zealand, the United Kingdom, and Canada. The book is a broad survey of notable individuals, important terms, significant events, and major organizations in music education. Terms include teaching methods, dances, instruments, genres, and songs. There are both basic concepts familiar to music educators and less familiar terms from many geographical areas including ausdance, clansongs, and corroboree from Australia. The dictionary does not try to describe all the instruments in the orchestra and band, but has interesting entries on ethnic instruments; for example, cruit (Scotland), djembe and didgeridoo (Aus.), and bodhran (UK). There are technical terms like iPod and iTunes. There are descriptions of types of ensembles (fife and drum, wind ensemble) and specific ensembles: The Seekers (Aus.), Grenadier Guards (UK), and the St. Olaf Lutheran Choir (USA). There are tests: Seashore Measures of Musical Talent and Gordon's Musical Aptitude Profile. Teaching methods include Sol-fa, Orff, Suzuki, Dalcroze, etc. This book is more comprehensive than most music dictionaries. Important individuals are included from the fields of music education, music therapy, musicology, performance, composing, and psychology. The majority of biographical entries are from the USA, but there are considerable entries from Australia and New Zealand, the United Kingdom, and Canada, plus entries for Europeans who have had an international impact. Women music educators are recognized: 27% of the entries from the USA, 34% from Australia/New Zealand, 13% from the UK, and 23% from Canada. …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.079 | 0.055 |
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".