Bibliographic record
Abstract
“Trans-” ideas — such as transgender, transnation, translation, and transculture — are being redefined in current research, and their full potential as critical categories is coming into view. Stryker, Currah, and Moore propose, for instance, that “transgender” should be seen not only as a descriptive term for identity, but as a valuable tool for dismantling the violence of the binary system and transcending traditional paradigm. In this article, I explore the possibilities of the prefix “trans” as a tool to dismantle discriminatory binary oppositions in Japanese Canadian writer Hiromi Goto’s novels. I argue that Goto creates transing spaces for her inbetweeners, or monsters. By claiming territory and affirming the value of liminal spaces for outcasts and misfits, those regarded as aliens or monsters can finally be at ease and at home. I also propose that the many dysfunctional families described in Goto’s novels are not only immigrant but transnational families that have to deal with transcultural politics to understand each other. Throughout my reading of the novels, the spatial-temporal dimensions of trans-ideas are stressed and demonstrated.
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 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.006 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".