Accidental Collocations: Re-Animating the Legacy of Sitting Bull / Collocations accidentelles : raviver le patrimoine de Sitting Bull
Bibliographic record
Abstract
Te HBO television series Deadwood popularized the Black Hills Gold Rush of the late nineteenth century, which saw an infux of miners and settlers move into South Dakota.Set in the time of great upheaval between the local Indigenous populations and newcomers, the show dwells only on stories of the town, rather than the larger moment in history that was about to unfold.Te discovery of gold in the traditional and treaty lands of the Sioux brought, under the protection of the US military, miners and settlers who sought to displace the Indigenous population despite the supposed protection of treaties.Resistance to this onslaught was led by Hunkpapa Lakota leader Tȟatȟáŋka Íyotake, or Chief Sitting Bull, culminating in the Battle of the Little Bighorn in 1876.Af er his warriors defeated Lieutenant-Colonel George Armstrong Custer and his army of 262 men, Sitting Bull and his Lakota followers fed across the US-Canada border, efectively preventing American pursuit, and settled in southwestern Saskatchewan in 1877.Four years later, caught in a diplomatic row between the Canadian and American governments, his people starving, Sitting Bull conceded and crossed back over the border from Wood Mountain, Saskatchewan, to the Standing Rock Agency Reservation in North Dakota.From there, Sitting Bull continued to oppose the American government's violation of the 1868 Treaty of Fort Laramie until he was killed in crossf re between the Lakota and the reservation police in December 1890.Like no other Native American, Sitting Bull achieved immense fame during, and long af er, his lifetime.While this essay does not focus specif cally on Sitting Bull, the story begins and ends with him, as NGC Review/Revue du MBAC, 2023.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.013 | 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".