Bibliographic record
Abstract
Ha dedicado la totalidad de su vida profesional - desde su grado inicial como Profesora Nacional Superior de Piano, logrado en el Conservatorio Nacional de Musica de Buenos Aires - al vasto tema de la Educacion Musical. Ha ensenado en todos los niveles y modalidades de la escolaridad general y se ha desempenado como Docente de Pedagogia, Didactica y Metodologia de la Ensenanza en el Conservatorio mencionado y en Institutos y Universidades de su pais y del extranjero, incluyendo USA y Canada. Directora del Instituto Superior de Arte del Teatro Colon de Buenos Aires durante diez anos, su labor al frente de tan importante institucion de formacion artistica se ha visto coronada por el exito. En 1996, logro su titulo de Doctora en Musica (PhD) con especialidad en Educacion, con su Tesis “Metodologia Comparada de la Educacion Musical”. Es autora de numerosos libros y articulos, varios de ellos publicados en ingles, espanol, frances, portugues y vasco. En 2012, edito con Wayne Bowman, The Oxford Handbook of Philosophy in Music Education; en 2019, escribio el capitulo sobe Sudamerica en The Oxford Handbook of Assessment Policy and Practice in Music Education (Ed. Tim Brophy). Presenta continuamente ponencias y trabajos de investigacion ante Seminarios y Congresos Argentinos e Internacionales, tanto en Latinoamerica como en Asia, Africa, America del Norte, Australia y Europa. Es miembro de numero de la Academia Nacional de Educacion de la Republica Argentina desde 2000.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.072 | 0.045 |
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".