Dis/locating Preferential Memory within Settler Colonial Landscapes: A Forward-Looking Backward Glance at Memoration’s Per/formation
Bibliographic record
Abstract
The degree to which authorized sites of commemoration such as monuments perpetuate deep-rooted practices of selective remembering and forgetting in settler states, and in doing so, help to entrench narratives and mythologies that mask ongoing colonial occupation and violence while denying Indigenous sovereignty, has arguably never been more evident. While official sites of remembrance undoubtedly shape the dominant imaginary, vernacular forms of commemoration exerted implicitly and explicitly in everyday life are also powerfully influential in the circulation of the nation’s ascendant ideation as what Audra Simpson calls “narration[s] of truth.” This paper will examine the ways “memoration,” an artistic/performance methodology I have developed through my inter-media art practice and scholarship, performs interventions into commemoration in the guise of public, national, and personal memory. As an adaptable and inherently relational, embodied and place-based methodology, memoration offers a framework of in Andrew Herscher's terms, “remembering otherwise”: one that activates a reckoning with the intergenerational responsibilities of being-in-relation, in my case as a white settler, on Indigenous lands that are at the same time “occupied” and unceded.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.027 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".