How Allotment-Era Literature Can Inform Current Controversies about Tribal Jurisdiction and Reservation Diminishment
Bibliographic record
Abstract
In a previous piece, ‘Unjustifiable Expectations: Laying to Rest the Ghosts of Allotment-Era Settlers,’ the author argued that a review of historical newspaper articles showed that the expectations of non-Indians who purchased lands on Sioux reservations in South Dakota during the allotment era above tribes disappearing were not justifiable because they were rooted in an expectation of continued injustice towards tribes. The article thus concluded that the Supreme Court should not presume that allotment-era settlers had justifiable expectations when it decided reservation diminishment and tribal jurisdiction cases. This article addresses whether allotment era literature pertaining to Sioux peoples can similarly help inform such cases. Although the results are more mixed, particularly with non-Indian-authored fiction, the works of Native writers such as Luther Standing Bear, Charles Eastman, and particularly Zitkala-Ša are helpful in explicating the injustices in the federal government’s land dealings with tribes, as was a nonfiction work by non-Native historian and poet Doane Robinson.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.008 | 0.041 |
| Scholarly communication | 0.009 | 0.016 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 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".