Bibliographic record
Abstract
This article takes a historicizing and structural approach to anti-Chinese racism, a stream of anti-Asian racism, understood as a system of meaning making for power advantages in changing contexts (Hall 2021[1997]). Based on textual data, observations, and interviews and drawing on literature on scapegoat racism and the sacrificial politics of threat and security (Girard 2021[1977]), it advances the following arguments: first, current discussions about anti-Asian racism are often narrowly focused on individual acts of hateful attacks, overlooking the anti- Chinese scapegoating discourse that is at the root of discriminatory and hostile treatment of the Chinese, particularly those with Mainland Chinese background. Second, the anti-Chinese scapegoating discourse has revived the anti-Communist Sinophobia during the Cold War with exaggerated claims about the threat of China and perceives the “Bad Chinese” in the Chinese diaspora as threats to Canada. Third, the anti-Chinese scapegoating discourse not only fuels racist and discriminatory treatment of the Chinese, it also diverts our attention away from serious issues in Canada that do not have much to do with China or the Chinese diaspora.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.005 | 0.003 |
| 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".