Allotment Stories: Indigenous Land Relations Under Settler Siege , ed. by Daniel Heath Justice and Jean M. O'Brien (review)
Bibliographic record
Abstract
Reviewed by: Allotment Stories: Indigenous Land Relations Under Settler Siege, ed. by Daniel Heath Justice and Jean M. O'Brien Katie Walkiewicz Allotment Stories: Indigenous Land Relations Under Settler Siege, edited by Daniel Heath Justice and Jean M. O'Brien. Minneapolis: University of Minnesota Press, 2022. 369 pp. Paperback, $28.00. My family's allotments skirt the northern edge of Township 29 North, Range 21 East on the Cherokee Nation plat maps. Their plots of farmland sit clustered together, just west of the Neosho River and Miami Nation. Multiple generations of women in my family fought to keep this land they collectively farmed intact, thus keeping the family together. Eventually, my great-grandmother Mary would relocate about one hundred miles southwest to Tulsa, where my grandfather and mother were born. I grew up even farther, about 170 miles away in Stillwater, Oklahoma, and have never visited that land. However, looking at the aerial views, I can envision what it looks and smells like. I can hear the locust (cicada) songs that are the iconic playlist of thick, humid nights in that part of the world, and which ᎢᏯ ᏗᎯ / iya dihi / Candessa Tehee (Cherokee Nation) describes in her contribution to Allotment Stories. But the point stands: I've never been to my family's allotment land, and alienation from that place brought a cascade of other distances that produced a kind of forced exile-in-proximity across two generations of my family. I start with my own allotment experience because, as Allotment Stories convincingly demonstrates, for many people who endured the dispossessive violence of allotment, those personal stories are what we have. Moreover, one of the aims of allotment was to flatten, if not entirely erase, Indigenous specificity by attempting to quantify Indigeneity via blood and force Indigenous people to operate as liberal colonial subjects rather than as members of robust Indigenous nations and kinship networks. As Darren O'Toole (Métis) poignantly reminds us in his contribution to this volume, the "transition from a people to a population is one of the most important knowledge-power relations [End Page 388] underlying the policy of allotment" (81). Allotment sought to break the stories, like the land, because they tell us who we are and why that matters. In response, Allotment Stories emphasizes the power of narrative by assembling a collection of poetic, personally inflected essays and academic writings that show how and why the privatization of land is and always was about Indigenous dispossession. Just as critically, the collection celebrates the many ways Indigenous people have actively challenged allotment and continued to live and thrive in spite of settler colonization. Most of the contributors are themselves Indigenous, and the chorus of their collective voices is itself an irreverent reminder that Teddy Roosevelt's description of allotment as a "mighty pulverizing machine" (a quote referenced in the introduction and multiple essays) failed to destroy everything (xiii). Allotment Stories brings together an extensive range of essays on the topic and, in doing so, attends to the many scales of life transformed by privatization-as-dispossession. Throughout the various pieces, readers learn more about the slew of treaties, acts, and other formal and informal policies crafted to steal Indigenous land, fracture kinship networks, and erode Indigenous sovereignty. The range of Indigenous peoples, geographies, time periods, academic disciplines, genres of writing, and languages the collection represents is one of its greatest strengths. Leanne Betasamosake Simpson's interludes remind us that the settler-constructed dams, changed waterways, and stolen and developed land that accompanied allotment also had devastating impacts on more-than-human kin like Amik (beaver). Some of the most profound moments in the collection are when authors share the life stories of specific individuals, like Louisa, Pearl, Edna, Frank, Uncle Don, Mary, and Fanny. Learning how these relatives fought against allotment, not just the dividing up of land but also efforts to break up families and nations, offers readers a clear sense of what Justice and O'Brien term the "restorative resilience" of Indigenous people. However, restorative resilience takes many forms in Allotment Stories. Seemingly counterintuitive measures like privatized land purchases (Mustonen and Feodoroff) or the organization of corporations (Velaise), speak to Indigenous ingenuity and...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.015 | 0.003 |
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".