Bibliographic record
Abstract
Disappearing Moon Cafe by Sky Lee, a Chinese-Canadian writer, and Chinatown by Junghee Oh, a Korean writer, reveal the racial discrimination of the Other in the social and cultural context of Chinatown. This perspective is expressed through heroines of the two novels, Kae and an unnamed female character. In the process of exposing racial discrimination, Kae and ‘the girl’ become the mainstream speaking agents for the mostly silent and marginalized ethnic Chinese living in Canada and Korea. Kae, in Disappearing Moon Cafe, re-writes Chinese-Canadian history by using key historical events from 1892 to 1986. Through rewriting her family history and the historical accounts of the Chinese in Canada, she influenced her community to speak out against their present discrimination. In comparison, ‘the girl’ in Chinatown discloses a variety of racial discriminations. ‘The girl’ distances herself from her family members and observes the discrimination of the others: mostly Chinese and a Korean prostitute named Maggie. This includes Maggie’s mixed race daughter being racially victimized by ‘the girl’s grandmother. Through the perspective of an elementary school student, ‘the girl’ represents the transitional situation in Chinatown between the old culture represented by China and the new culture represented by America in the 1950s. Facing a confusing and contradictory world, ‘the girl’ realizes that there is nothing absolute and chooses an alternative life, one in which racism is no longer perpetuated. In brief, Kae and ‘the girl’ create a picture, which dispels prejudices against people of color, especially the Chinese, as social constructions, and in this picture we are presented with heroines exercising their agency, pursuing a more tolerant world, namely one without any racial prejudices.
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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.204 | 0.079 |
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".