Bibliographic record
Abstract
To the Academy is a multi-media performance work that makes poignant and humorous commentary about education, common paradigms of diversity, and the oppressive nature of institutional labor. Created through a dialogue between myself, an Indian American with training in various forms of physical theatre and Indian dance, and Guyanese-Canadian actor Marc Gomes, it has been performed at several universities and arts centers since 2015. In this essay, I will interrogate the ways in which we place select elements of “Indian tradition” at the service of the piece’s overarching theme of histories of European domination, asking whether making these cultural materials subservient to our political agenda constitutes a form of appropriation. I examine three components of the work: the character of the classical Indian dancer who appears in the first section of the show, the explicit references to the ancient Sanskrit treatise on performance, the Natyashastra, and the framing of both these elements within our adaptation of Franz Kafka’s story, “Report to an Academy,” about an ape who learns to impersonate humans. In so doing, I explore the ethical responsibilities artists of color have in working with intercultural aesthetics. Furthermore, I assert the inevitably ambivalent nature of activist performance, even if artists aim to resist hegemonic structures.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.018 | 0.010 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".