Pequot Warriors Combating Paper Genocide: How the Eastern Pequot Tribal Nation Uses Education to Resist Cultural Erasure
Bibliographic record
Abstract
This paper analyzes the southeastern Connecticut Eastern Pequot Tribal Nation’s battle with cultural erasure and resistance through education. Indigenous education programs are gradual yet the most effective method of resisting Western cultural erasure from the United States government, because they peacefully invite both Natives and non-Natives to learn about Native American history outside of European colonizer textbooks. The Tribe battles the erasure that can result from external parties’ ability to grant state or federal titles recognizing tribal authority (known as recognition titles) to determine who receives the powerful stamp of Indigeneity and the right to self- govern. My case study focuses on the Eastern Pequots Archaeology Field School project in collaboration with University of Massachusetts, Boston. I evaluate how the Eastern Pequots use a collaborative archaeology education program with their Tribal members and non-Native individuals to resist erasure by decolonizing Western pedagogy. The Field School has gathered over 99,000 artifacts over 15 seasons that dismantle common misconceptions of how Native Americans lived during the beginning of the United States’ history and redefine modern beliefs about how Natives survived European colonization. The Field School contributes to expanding brief descriptions of Native history into a more complicated and dynamic story that elaborates on Native struggle, survival and resistance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.015 | 0.010 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".