Beyond Celebration: A Critical Sociological Inquiry of Interracial Union Research in Canada
Bibliographic record
Abstract
Research in Canada has latched onto the explanation that the growth in interracial unions is both cause and consequence of progressive social change. The authors cast a skeptical eye on this regime of truth produced by positivistic statistical procedure and the speculative theory of social change as it connects to the state brand of tolerance. They offer a meta-theoretical critique that challenges the taken-for-granted assumption that the growth in interracial unions can be explained by multiculturalism, the presumed antiracist implications of these unions, and the supposed amorphousness that mixed-race children presumably embody. The authors seek to disrupt the mystification and commodification of interracial unions and the children of these unions as proxies for social change. Instead, they call for critically and historically informed qualitative and quantitative research that avoids deputizing interracial unions en masse as societal change agents. They call for other and more robust metrics for measuring the reduction of racism. Attendant to radically altering how interracial unions are imagined, it is necessary for theory and research practices to deeply engage with class, gender, race, sexuality, and asymmetries of power when examining the complex, contradictory, and joyous inner and outer life-worlds of interracial couples and families.
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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 | 0.018 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.129 | 0.075 |
| Scholarly communication | 0.024 | 0.008 |
| Open science | 0.008 | 0.014 |
| Research integrity | 0.007 | 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".