Bibliographic record
Abstract
Globalization has prompted a multicultural retheorization of both consumer and market (Fu et al., 2014; Riefler et al., 2012; Kipnis et al., 2019). In short, the Canadian multicultural market consists of international goods which are authentic, domestic goods purporting authenticity (e.g., orientalizing), and multinational, fusion innovations that are authentic-ish (e.g., self-orientalizing; Hui, 2019; Li, 2020; Stephens, 2021). What results is a fetishistic commercialization of multiculturalism, where brands are packaging ethnicity and race to vie for consumer attention. This paper addresses the latter variety — self-orientalist packaging designed for products born out of (formerly) Chinese Canadian enterprises, namely Wong Wing Fried Rice. Developing an analytical and theoretical approach that can support the identification of racialization, racist typologies are situated in graphic design. Themes derived from the analysis include racism, orientalism, self-orientalism, exoticism, cultural appropriation, among others. Findings reveal how self-orientalist packaging and label design is discursively negotiated as both internalized racism and anti-racist resistance, necessitating a more nuanced approach that reflects the sociopolitical context in which products are branded. The adoption of transversalist tenets, an anti-racist modality outlined by the methodological component of this study, presents one possibility.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.008 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".