Performativity, Possibility, and Land Acknowledgments in Academia: Community-Engaged Work as Decolonial Praxis in the COVID-19 Context
Bibliographic record
Abstract
At the intersection of dance, performance, and Indigenous studies, this essay reflects on how an assistant professor at the University of California, Los Angeles—with the support of a graduate student researcher—has aimed to put an Indigenous land acknowledgment into praxis through community-engaged work. In academic settings, land acknowledgments are often given prior to an event and may circulate on written materials, such as event programs, syllabi, letterhead, departmental and research centre websites, and email signatures. Based on Indigenous protocols, these statements typically identify the original Indigenous peoples whose land the university currently occupies; they should also be created in collaboration with Indigenous leaders from the tribe(s). Indigenous land acknowledgments can be important because they directly combat the injustice of settler-capitalist, mainstream discourses that often obscure Indigenous peoples and practices or relegate them to the historical past. Yet, Indigenous people and Indigenous studies scholars have critiqued non-Native land acknowledgments as “performative.” Without direct material benefits to Indigenous peoples, land acknowledgments can serve as empty gestures that “perform” university commitments to anti-racism, equity, diversity, and inclusion. In contrast to the “performative” as an empty gesture, the fields of performance and dance studies frequently theorize “performativity” as a material action that can function both hegemonically and subversively. This essay argues that community-engaged research, teaching, and service—which the authors view holistically—are key ways to begin or further the process of putting a university’s land acknowledgment into action.
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 | 0.011 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.031 | 0.110 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.002 | 0.019 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".