Review Of "Dancing Indigenous Worlds: Choreographies Of Relation" By J. Shea Murphy
Bibliographic record
Abstract
In this book, her second, Shea Murphy (Univ. of California, Riverside) extends a trajectory she began in her first book, The People Have Never Stopped Dancing: Native American Modern Dance Histories (CH, Jun'08, 45-5492). She deepens discussions regarding Indigenous knowledge grounded in relationality and the ways such understandings permeate current Indigenous dance practices. Sharing examples from some 20 years of research—including conversations with Indigenous dancers and communities in Aoteroa, Australia, and North America—Shea Murphy presents important perspectives. She asserts the importance of the voices of Indigenous dance artists within the larger field of dance studies. Writing as a non-Indigenous scholar, she documents, in dialogue with her Indigenous colleagues, practices that re-center their creative projects in opposition to past colonialist norms. The author draws on and incorporates arguments from other decolonizing writing in dance theory, anthropology, and philosophy. She investigates ways indigenous environments and creations manifest in California, Minnesota, New York, and Ontario, as well as Aoteroa. The widely varied contexts demonstrate the vibrancy of current respectful, relational, Indigenous choreographies. Inclusion of writing by and interviews with Indigenous collaborators further supports the author’s approach. Useful notes and an extensive bibliography ground and augment the text. Helpful photos are included. Valuable for scholars of dance, Indigenous studies, and anthropology. Summing Up: Highly recommended. Graduate students, researchers, faculty.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.010 | 0.019 |
| Science and technology studies | 0.005 | 0.007 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".