Bibliographic record
Abstract
Asian Canadian writer Larissa Lai reflects, in an article written eight years after the publication of her first novel, When Fox Is a Thousand , that the central character, Artemis, ‘is a product of my thinking through what happens to young Asian Canadian women in the absence of a radical community-based identity politic. She has some awareness of colonialism and white privilege, and some awareness of how her body is read within mainstream white society, but she does not really have any useful tools to deal with this knowledge’ (2005: 168). This article explores Lai's ‘thinking through’ the issue of white visualising practices that ‘read’ the Asian female body as a hyper-feminised, doll-like other in her most recently released book, Automaton Diaries (2009). This article will focus upon the consistent return within her body of fiction and poetry to the figure of the Replicant Rachel from Ridley Scott's Blade Runner. Lai's implicit questioning of Rachel's fixed subject positioning in Blade Runner indicates that her body of work is part of a wider project; for while under-taking a project of redress, more fundamental to Lai's politics of identity is the notion of address. This paper argues that Lai's answer in Automaton Diaries to Deckard's well-known question in Blade Runner - ‘How can it not know what it is?’ - is a vision of Rachel turning around and looking within and across the differing paradigmatic structures of cinema, literature, art, photography and all its attendant criticism. This glance back (and at) these structures foregrounds the transformative values available to the subject who looks at and records her life through her own eyes, overtly challenging the elisions and silences of white patriarchal inscriptions of subjectivity that would otherwise place her (like the character of Artemis in Fox ) as a racialised, voiceless, doll-like object of white privilege and desire.
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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 | 0.018 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.014 |
| Science and technology studies | 0.082 | 0.099 |
| Scholarly communication | 0.046 | 0.016 |
| Open science | 0.006 | 0.016 |
| Research integrity | 0.009 | 0.015 |
| Insufficient payload (model declined to judge) | 0.012 | 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".