Bibliographic record
Abstract
By shifting the focus from policy to people, Allotment Stories offers a fresh and deeply compelling retelling of the privatization of Indigenous lands. The book eschews mooring discussions of allotment in the 1887 Dawes Act in the United States. Instead, this collection of more than two dozen community-centered essays or creative contributions showcases how Indigenous people across the globe resiliently grappled with and responded to the consequences of a variety of efforts at land privatization. The book has a broad geographic and temporal scope. Many contributions focus on the continental United States and Canada, but the collection also includes essays that address Aotearoa New Zealand, Mexico, Palestine, Hawaii, Alaska, the Sápmi region of northern Europe, and Guam. Although a few take on earlier periods, most essays examine nineteenth-century, twentieth-century, or contemporary contexts. Taken together, the essays testify to diverse and dynamic Indigenous resistance, restoration, and resurgence in the face of shared experiences of land privatization.The collection is organized into four thematic sections. The first draws on the family stories of the contributors to trace the results of land privatization within families over time and includes notable contributions from the book’s editors, Daniel Heath Justice and Jean M. O’Brien. O’Brien’s essay, for example, incorporates her grandmother’s writings to show how allotment when coupled with Ojibwe traditions of mobility unexpectedly spurred the creation of new, vibrant Indigenous communities. Sections 2 and 3 examine divisive entanglements of land privatization with white supremacy, gendered ideologies, and state violence. Finally, essays in the last section more directly address themes of Indigenous resistance and resurgence that run through the whole book. The book is an admirable model of how a multidisciplinary collection can illuminate the deeply intimate and relational dimensions of a legal and administrative policy.The collection is thoughtfully situated to address the current moment. Speaking to the growing field of global Indigenous studies, the book is well suited to the classroom and includes a glossary of key terms for nonspecialists. Many authors also directly address contemporary political issues and ongoing efforts to privatize Indigenous lands, and an afterword by Stacy L. Leeds contextualizes the recent decision of the Supreme Court in McGirt v. Oklahoma. Although the essays gravitate around colonial efforts to divide and dispossess, Indigenous resiliency represents the dominant theme of this timely and enriching collection.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.007 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".