Micro-practices of nation-building: race and class in Jennifer Elrick’s <i>Making Middle-Class Multiculturalism</i>
Bibliographic record
Abstract
How do race and class intersect in state practices of nation-building? This is one of the key themes in Jennifer Elrick’s book Making Middle-Class Multiculturalism: Immigration Bureaucrats and Policymaking in Postwar Canada. In this essay, I discuss Elrick’s conceptualization of the relation between race and class, which combines notions of class as a component of race on the one hand, and class as intersecting with race on the other hand. I argue that the intersectional perspective is most convincing. Elrick shows that the cultural and moral traits which bureaucrats ascribe to applicants – integrity, ambition, trustworthiness, initiative and self-reliance – are part of both racial classification systems and class classification systems. I therefore conclude by proposing to think of the intersection of class and race in state classificatory practices as consisting in an overlap in the criteria for allocating individuals to the categories of class and race.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.018 | 0.043 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 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".