The Surge Toward “Diversity”: Interest Convergence and Performative “Wokeness” in Music Institutions
Bibliographic record
Abstract
Following the brutal murder of George Floyd by police office Derek Chauvin in summer 2020, interest in so-called “diversity” initiatives in schools of music across the U.S. and Canada has exploded. In this article, I put forward Derrick Bell’s (1995) principle of interest convergence—a key tenet of critical race theory (CRT)— in order to explore a possible convergence of interests in “diversity work” between white and Black, Indigenous, and People of Color (BIPOC) groups in higher education music institutions. I examine music institutions’ performances of “wokeness” at this time and then consider what Sara Ahmed (2012) calls the “nonperformative” to interrogate the convergence of white interests with the interests of BIPOC communities. To conclude, I put forward ways to capitalize on this interest convergence through curricular and policy change in higher education music institutions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 teacher head, 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".