Bibliographic record
Abstract
How can the body which is constantly changing inspire understanding about life and about knowledge? I am inspired by memories of seeing and participating in dance that felt inclusive. These memories remind me that dance can be a gift, to both the participant and the observer, of a sense of freedom, agency and collective. The left wing modern dance movement in New York, toyi-toyi from the South African anti-apartheid movement, and radical cheerleading at a protest of the Free Trade Area of the Americas are all examples of this. I want to draw from this understanding of dance in order to allow for feelings of abundance, empowerment and agency in my writing about the dancing body and hope. I am filled with a sense of the possibilities for history and memory in subverting hegemony through the dancing body. I can see how history or memory also embodies the on-going creation of the landscapes of the present. It is not just the constructed narratives that those with the power to do so produce about themselves and others' pasts. I want to bring some of life's patchiness into my own attempt to tell a story based on a different writing structure so that I might play with structure in a way that breaks with modern ideas of progress and knowledge production. The story itself has something to do with the body, memory and dance. Part of my goal is to adopt a writing style that mimics this story.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.016 | 0.029 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.012 | 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".