Non-linearity and inter-referencing : orienting towards an Asian settler of color poetics of decoloniality in Iron goddess of mercy and About time
Bibliographic record
Abstract
This thesis close reads Larissa Lai’s long poem Iron Goddess of Mercy (2021) and Jin-me Yoon’s videography and photography exhibit About Time (2022) as Asian Canadian cultural productions that critique the settler colonial state through positioning the Asian settler of color and their histories in relation to those of Indigenous peoples. This project is motivated by the overarching question of: What frameworks can open up Asian-Indigenous relations of solidarity and disrupt settler colonial impasses? Contextualizing the term “settler of color,” specifically in relation to Asian settlers, Chen Kuang-Hsing’s concept of “inter-referencing” is joined with frameworks of non-linearity as methodology for opening up possibilities for Asian and Indigenous coalition and orienting towards each other as reference points, rather than centering the West. The first chapter examines Larissa Lai’s inter-referencing of non-linear frameworks through the Taoist I Ching and Stó:lō practices of remembering with direction as a starting point that produces an alternative inventory of empire and further generates potential points for Asian and Indigenous inter-referencing. The second chapter focuses on the imagery of digging across Jin-me Yoon’s works as a framework of “vertical time” that uncovers overlapping histories of empire and shared reference points through place-based methodology, as well as her imagery of mound-building as an honoring of the excavated histories and as a Korean and Coast Salish reference point in itself. Ultimately, this thesis argues that non-linear reorganizations of time can alchemize our different but always interconnected positions and histories into overlapping genealogies of empire and foster Asian-Indigenous practices of inter-referencing that produce alternative epistemologies and methodologies and, in turn, disrupt colonial impasses.
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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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.014 | 0.031 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".