Bibliographic record
Abstract
Recent scholarship examines how improvisational pedagogy can model ethical social practices and relations (Heble and Waterman 2008). A parallel strain of literature highlights the importance of decolonizing Western knowledge systems through more global pedagogies (De Lissevoy 2010; Nakata 2012). Little work, however, considers the role of improvisation in decolonizing the institution. In this essay, I examine the role of intercultural improvisation as a critical decolonial pedagogy and practice within the neoliberal university through an auto-ethnographic reading of my experience as performer in the ImprovisAsians! 2017 Festival. Hosted by Asian Improv ARts, a pan-Asian musical collective dedicated to Asian American activism and community building, this week-long festival brought together artist-scholars of color for a series of collaborative performances and discussions on the campus of San Francisco State University, the generative site of the 1968 Third World Liberation Front Strikes (TWLF). Almost fifty years later, this institutional space was my entry point into what Asian Improv artists Francis Wong and Hafez Modirzadeh describe as their “sound come-unity,” an ethical collective built and sustained through musical sound. Following George Lewis’ call for an improvisational pedagogy as critical methodology (2008), I explore the musical intimacies and listening strategies of this particular “sound community” within the context of discourses on academic diversity and inclusion. I argue that intercultural improvisation serves as a site of fugitive planning and collaborative study with the potential to decolonize institutional epistemologies.
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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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.012 | 0.032 |
| Scholarly communication | 0.012 | 0.013 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.021 | 0.003 |
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".