What has Barack Obama’s election victory got to do with race? A closer look at post-racial rhetoric and its implication for antiracism education
Bibliographic record
Abstract
Our charge in this article is that it is becoming almost impossible to speak about race after Obama’s election victory because for many Canadians and Americans, the election of Barack Hussein Obama as the first African American President of the United States ushered the US into a post-racial era. This thinking not only obfuscates any discussion about race and racism but also ignores the historical and contemporary evidence of racism in the United States. For those of us living in Canada, we cannot help but examine the post-racial rhetoric and its implications for antiracism education in Canada and the United States. The article asks these questions: if race is analytically reductive and has no intellectual validity, then what is the social significance of race in the era ushered in by Obama’s election victory. How do we address the limits and possibilities of defining race as an ascribed status linked with physical characteristics of skin colour and pigmentation while engaging race and social difference in a power and conflict analysis? How do we contextualize concepts such as ‘race,’ ‘racism,’ and ‘post-raciality’ to the broader process of institutional and structural transformation in the era ushered in by Obama’s election victory? Our article invites complex and multiple discussions on these questions and their implication for antiracism education in Canada and the United States.
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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 | 0.010 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.033 | 0.040 |
| Scholarly communication | 0.017 | 0.010 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.013 |
| Insufficient payload (model declined to judge) | 0.004 | 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".