Bibliographic record
Abstract
“Yeah I know that you wanna be Canadian, please.” This is the opening line of the 2009 “Canada Day” YouTube music video by Julia Bentley and Andrew Gunadie that went viral days after it was posted. The video is a kitsch anthem celebrating the benefits of Canadian identity, but there is a deeper message in it, and, indeed, in the troubling responses that it initiated, that makes it a ground-breaking text in Canadian cultural discourse about national identity and anti-racism. The YouTube site invited responses from viewers, and soon became flooded with racist slurs aimed at Gunadie’s Asian descent and his questionable right to claim to “be” Canadian. In short, the very public space of YouTube became a disturbing site of intimate violence. The backlash against the video was so extreme and unsettling that it led to a CBC news investigation, in which Gunadie described the racism the video inspired and his equally “inspired” YouTube fight against the racists. Fed up with being subjected to online violence, Gunadie retaliated by creating a number of ingenious videos. His responses did not resolve intimate and uncomfortable moments into invisibility. On the contrary, the discomfort of online racism prompted from him a self-consciously “uncomfortable” affective response. These cultural texts stand as a powerful testament to the mediating force of online exchanges as a forum in which debates about national and transnational identities are being waged.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".