Bibliographic record
Abstract
Back in 1988, my father had passed away and for a short time I stopped dancing. I lost interest, I lost my heart, you know…' cause I used to dance on stage for my father…Recently,…I just started back dancing…the reason is 'cause…This is me. This is who I am. A dancer. A Fancy dancer at that. And this is where I felt my father was most proud of me. And even now, I still feel that he is most proud of me. Right now, he's with me when I dance and I dance for my father. I dance for him, for my pop, for Gray Fox. —Calvin Burns (Cherokee) (Traveling the Distance) Throughout the United States and Canada people go to Native American intertribal powwows. At powwows women, men, and children execute rhythmic movement, drumming, and song as they experience and express sensory stimuli. The aroma of sage incense pervades as Indians and non-Indians socialize, share fry bread, and sell, buy, or “window shop” at concession stands. Vendors display T-shirts, fur, turquoise, silver, and beaded jewelry as well as artwork, CDs, and Ecuadorian and Peruvian items. All participants enjoy storytelling, comedy skits, Indian rap and country music performances, and Mexican “Aztec” dance presentations alongside rituals such as giveaways, the Eagle ceremony, or the Veterans dance. These social and spiritual celebrations occur in increasing numbers across North America. In the tristate region of New York, New Jersey, and Connecticut alone, from 1995 to 2001, the number of powwow events has tripled.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.007 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".