<i>Tlingipino Bingo</i> , settler colonialism and other futures
Bibliographic record
Abstract
We present an analysis of Tlingipino Bingo, which is the latest iteration of our on-going experiment to work with performance as a means of translating and transforming scholarly work to generate more informed and nuanced public debate about migrant labour. Tlingipino Bingo was a collaboration between white settler academics and Filipino and Tlingit artists in Whitehorse Canada, created in a context of rapid Filipino migration and racialised tensions between Filipino migrants and First Nations peoples in Whitehorse. It brought the communities together to participate in an interactive bingo game and to exchange stories of disparate but resonate experiences of colonialism. We document the public event of Tlingipino Bingo to interrogate how deeply settler colonialism burrows into everyday life, including practices of racialised immigrants, the ways that a model minority discourse functions within state multiculturalism, and to imagine other futures beyond settler colonialism, which could possibly include white settlers as allies. We venture that the performance might also help to think strategically about critical responses to contemporary claims of dispossession by white citizens in Canada and elsewhere, as well as their destructive nostalgia for a lost national time of whiteness.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.017 | 0.012 |
| Scholarly communication | 0.008 | 0.002 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".