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Record W2767264489 · doi:10.1177/0263775817730699

<i>Tlingipino Bingo</i> , settler colonialism and other futures

2017· article· en· W2767264489 on OpenAlexaffabout
Caleb Johnston, Geraldine Pratt

Bibliographic record

VenueEnvironment and Planning D Society and Space · 2017
Typearticle
Languageen
FieldSocial Sciences
TopicSoutheast Asian Sociopolitical Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsColonialismMulticulturalismFutures contractWhite (mutation)Context (archaeology)SociologyImmigrationGender studiesState (computer science)Media studiesPolitical scienceHistoryLaw

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.626
Threshold uncertainty score0.998

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0030.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.025
GPT teacher head0.288
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2017
Admission routes2
Has abstractyes

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