Bibliographic record
Abstract
Mixed race scholarship considers the deployment of the term “mixed race” as an identification and theorizes that the operation of the external racial gaze is signaled through the “what are you?” question that mixed race people face in their everyday lives. In interviews conducted with mixed race, young adults in a Western Canadian urban context, it was evident that the “what are you?” question is the verbal form of the external racial gaze’s production of ambivalence on mixed race bodies. However, this study also found that mixed race people have “ready” identity narratives in response to the “what are you?” question. This paper shows the importance of these narratives (the very existence of the “ready” narratives, as well as the content of the “ready” narrative) for fleshing out the operation of the external racial gaze in the Canadian context. Respondents draw on two closely related modes of narrating origin when responding to the “what are you?” question: they respond through a kinship narrative that is heteronormative and they narrate that they inherit “national origin” “through blood.” I argue that these responses point to how the gaze produces the multiracialized body through the desire to imagine and “know” its originary point of racial mixing. Yet, the “ready” narratives are also agential: while at times they narrate to the expectations of the gaze, they also “play on” the gaze.
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 | 0.009 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.023 | 0.021 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| 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".