Song as a Catalyst for Change: Teaching for Equity and Social Justice
Bibliographic record
Abstract
As participants in the Lincoln Center Institute (LCI) Teacher Education Collaborative (TEC) in aesthetic education, based on the work of philosopher Maxine Greene, we use inquiry-driven engagemen t with works of art to open our minds and hearts to imagining the world as if it could be otherwise. LCI teaching artist and teacher educator plan the experiential class that precedes engagement with the performance (or exhibit) and develop an overriding question to guide the experience. Additional questions grow out of our shared inquiry, comments, discussion, reflections, and research during or after engagement with the work of art. Mind, body, and imagination are involved during the experiential class to prepare students for the inquiry-driven engagement. In this paper I share my experience, as a teacher-educator, and that of my teacher education students as participants in inquiry-driven engagement with a number of works of art. In particular, I focus on inqu.iry-driven engagement with a performance featuring internationally known storyteller and singer, David Gonzalez, whose work grows out of his Puerto Rican heritage. The overriding questions in my inquiry into this experience ask if and how song, when viewed through the eyes of a participant in aesthetic education, can lead to discoveries about ourselves, others, our teaching and learning, and the world. Can song be a catalyst for change, for teaching for equity and social justice? Contextual Background The context of my teaching and participation in the aesthetic education collaborative The demography of North America is changing as immigrant populations continue to come to Canada and the US. Moves within cultures can be difficult; moves across cultures can be even more challenging. Such moves often position people as the other in society and deny them equality of opportunity. Even within mainstream society, some members are subjected to biases and prejudices.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.014 | 0.028 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".