Measuring race and ethnicity in the censuses of Australia, Canada, and the United States: Parallels and paradoxes
Bibliographic record
Abstract
Most national censuses include questions about race, colour, national origins, ethnicity, ancestry, and tribe in an effort to describe subgroups within their population. In this paper, we focus on changes over the last half-century in the racial and ethnic classification schemes of the censuses in three countries that share important historical and demographic features Australia, Canada, and the United States. We show that there are similarities, as well as some idiosyncratic features, in how these three nations define and describe racial and ethnic subgroups. We then argue that the gathering of data on the racial/ethnic subgroups in these three nations has followed a similar progression over the last half-century because of shifts in the understanding of race and ethnicity, data-gathering procedures, and the ongoing dialogue between each national population and its data-gathering institution.
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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 | 0.019 | 0.084 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.008 | 0.017 |
| Science and technology studies | 0.006 | 0.014 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".