Bibliographic record
Abstract
The Sankofa Drum and Dance Ensemble was a Ghanaian music ensemble that focused on Ewe music. I founded this ensemble in the elementary school where I taught in the Toronto area. From the time of its founding, one of the overarching goals I had for the group was a disruption of images and stereotypes that the students held of Africa in general, and more specifically, of Ghana. How did the students perceive Africa? Did the ensemble change their perceptions at all? How did my own positionality, as a white, Western woman enter the picture? How did students’ ensemble participation affect them politically? During the ensemble’s fourth year, I conducted a qualitative study to investigate these questions. I interviewed nine students from age 9 to 13 for their perceptions of the effects of their ensemble participation. This article examines the ways in which participation in the ensemble sustained stereotypes and media images as well as other images that commodify and exotify the Ghanaian culture. This article also investigates the ways in which music may disrupt these images. I conclude with implications for the place of the world music ensemble in music education, exploring both the political caveats that come with implementing such a program and potential ethical ways of creating world music ensembles in the public school system.
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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.021 | 0.026 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
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".