Bibliographic record
Abstract
I have spent the last twenty-plus years and counting researching and writing on Indigenous public history projects, most notably on the changing relationship between Indigenous people and museums.This research is featured in my book, Decolonizing Museums: Representing Native America in National and Tribal Museums, which focuses on the representation of Native Americans in museum exhibitions at the Smithsonian Institution's National Museum of the American Indian (NMAI), and two Anishinabe tribal museums (the Mille Lacs Indian Museum in Minnesota and the Ziibiwing Center of Anishinabe Culture and Lifeways in Michigan). 1 Through an analysis of exhibition texts and images, archival records related to exhibition development, and interviews with key staff members, I explore the changing representations of Indigenous history and memory in a diverse group of museums that hold significant Native American cultural resources.In my scholarship, I give considerable attention to the relatively recent museological shift from curator-controlled presentations of the Native American past to a more inclusive or collaborative process with Indigenous people actively involved in determining exhibition content.My work critically examines the complexities of this relationship-both the positive outcomes as well as the challenges that remain.Central to my analysis is exploring how museums can serve as sites of decolonization through honoring Indigenous knowledge and worldview, and by discussing the hard truths of colonization in exhibitions in an effort to move toward healing and understanding. 2 This process of examining the hard truths also needs to include critical self-examination on the part of colonial institutions regarding their relationship with Indigenous tribal nations and communities in the past and present.3
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.013 | 0.015 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".