Rethinking the Practice and Performance of Indigenous Land Acknowledgement
Bibliographic record
Abstract
In this article, Dylan Robinson, Kanonhsyonne Janice C. Hill, Armand Garnet Ruffo, Selena Couture, and Lisa Cooke Ravensbergen consider the performative and political efficacies of land acknowledgement. The article is an edited transcription of a plenary presentation at the Canadian Association for Theatre Research that took place on 30 May 2018 at the Isabel Bader Centre for the Performing Arts Concert Hall in the city now called Kingston, in the province now known as Ontario. Each panellist was to think through the performance—the what it is we do—of acknowledgement by responding to a series of questions. Questions posed to the panellists included: Does the acknowledgement of Indigenous lands and waterways elide the acknowledgement of other forms of structural and epistemic violence within the specific contexts we work in as academics and artists? How might acknowledgement be aligned with a politics of recognition that is a continuation of settler colonial logics rather than a break from them? What must occur for acts of acknowledgement to transform into actions that effect Indigenous sovereignty? How might acknowledgement be ‘actioned’ differently by settler Canadians, ‘arrivants,’ immigrants, displaced peoples, and visitors? How can standardized forms of acknowledgement give way to context- and site-specific forms of redress?
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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 | 0.016 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.008 | 0.046 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| 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".