Beyond Land Acknowledgment in Settler Institutions
Bibliographic record
Abstract
Abstract What does land acknowledgment do? Where does it come from? Where is it pointing? Existing literature, especially critiques by Indigenous scholars, unequivocally assert that settler land acknowledgments are problematic in their favoring of rhetoric over action. However, formal written statements may challenge institutions to recognize their complicity in settler colonialism and their institutional responsibilities to tribal sovereignty. Building on these critiques, particularly the writings of Métis cultural producer Chelsea Vowel, this article offers beyond as a framework for how institutional land acknowledgments can or cannot support Indigenous relationality, land pedagogy, and accountability to place and peoples. The authors describe the critical differences between Indigenous protocols of mutual recognition and settler practices of land acknowledgment. These Indigenous/settler differences illuminate an Indigenous perspective on what acknowledgments ought to accomplish. For example, Acjachemen/Tongva scholar Charles Sepulveda forwards the Tongva concept of Kuuyam, or guest, as “a reimagining of human relationships to place outside of the structures of settler colonialism.” What would it mean for a settler speaker of a land acknowledgment to say, “I am a visitor, and I hope to become a proper guest”? Two empirical examples are presented: the University of California, Los Angeles, where an acknowledgment was crafted in 2018; and the University of California, San Diego, where an acknowledgment is under way in 2020. The article concludes with beyond as a potential decolonial framework for land acknowledgment that recognizes Indigenous futures.
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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 | 0.010 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.013 | 0.038 |
| Scholarly communication | 0.015 | 0.011 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".