Notice bibliographique
Résumé
Songs that were once considered standard repertoire in elementary music programs across Canada are now being identified as including derogatory, misogynistic, and/or harmful texts. While there has been research and findings compiled on the text of songs (Bailey, 2020; Ellingsen, 2019; Kelly-McHale, 2018; McDougle, 2021), this is still a relatively new field, particularly regarding how information about texts of songs is shared with teachers. How can existing music education programs provide learning opportunities around repertoire selection for both in-service and preservice teachers? The Orff Level Certificate Program of Carl Orff Canada works with both preservice and in-service music teachers. The Orff program occurs on a yearly basis, with approximately 25 teacher educators and 250 teachers enrolled across the country. With the majority of elementary educators being “white, middle class, female, heterosexual teachers” (Holden & Kitchen, 2019, p. 27), there is a need to acknowledge the social hierarchy present in the classroom, that is, the power and privilege held by music educators. By working with music teachers to think critically about what musics to include, Orff Level Teacher Educators can provide the tools for music educators to “shape a curriculum and a pedagogy that purposefully places classroom musics alongside students’ own musics, experiences and interests (Hess, 2017, p. 71). In March 2021, an online survey, including a combination of multiple-choice and open-ended questions was sent to 25 Orff Level Teacher Educators in Canada. Responses were received from 17 teacher educators. The following questions guided the investigation: (a) How are songs selected for inclusion in the program? (b) Have there been changes to the repertoire list over the last five years? If so, what is driving these changes? (c) How do teacher educators see their selection process of repertoire impacting teachers’ choices of repertoire? Using thematic analysis, I analyzed the responses looking for common themes. These findings have served as a foundation for dialogue with Orff Teacher Educators. The next phase of the study will begin in spring 2022 where I will be interviewing three to five participants to further clarify findings from the survey portion of the research. References Bailey, P. (2020, April 27). Reclaiming kumbaya! https://www.decolonizingthemusicroom.com/reclaiming-kumbaya Ellingsen, A. (2019, October 30). Jump Jim Joe. https://www.decolonizingthemusicroom.com/jump-jim-joe Hess, J. (2017). Equity in Music Education: Why equity and social justice in music education? Music Educators Journal, 104(1), 71–73. https://doi.org/10.1177/0027432117714737 Holden, M., & Kitchen, J. (2019). Equitable admissions in Canadian teacher education: Where we are now, and where we might go. In J. Mueller, & J. Nickel, (Eds.) Globalization and diversity in education: What does it mean for Canadian teacher education? (23-60). Canadian Association for Teacher Education. Kelly-McHale, J. (2018). Equity in music education: Exclusionary practices in music education. Music Educators Journal, 104(3), 60–62. https://doi.org/10.1177/0027432117744755
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,008 | 0,038 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,004 | 0,002 |
| Études des sciences et des technologies | 0,005 | 0,009 |
| Communication savante | 0,008 | 0,005 |
| Science ouverte | 0,001 | 0,005 |
| Intégrité de la recherche | 0,001 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».