Using Onomastics to Inform Diversity Initiatives
Bibliographic record
Abstract
In multiracial societies, the diversity of names in the workforce may reflect racial inclusivity. There is scant data on racial representation among Canadian physicians, prompting our analysis of naming diversity. We profiled the race and gender demographics of the names of physicians in Canadian academic radiology departments. Further, we devised a structured classification methodology using a commercial artificial intelligence and naming database to classify 1,727 names according to national origin and gender. The names were retrieved from faculty websites. A Z-test of proportions was used to compare radiologists’ name demographics to demographics from the 2016 Canadian census. In close agreement with much of the literature on gender demographics, 31.99% of names were classified as female. Names that were classified as belonging to Indigenous, Black, Latin American, and Filipino name-bearers were underrepresented. Names classified as belonging to the following groups were overrepresented: South Asian, Chinese, Arab, Southeast Asian, West Asian, and Korean. Names associated with White subjects in the corpus were proportionally represented for full names and overrepresented for given names. Faculty with full names classified as Southeast Asian, Korean, and Chinese often had given names that fell into the White category. The structured methodology showed high inter-rater reliability for race classifications. The racial disparities we observed mirrored those found in surveys of medical students, suggesting that the bottleneck occurs at the level of medical school admissions. Thus, onomastics can provide valuable data to diversity initiatives.
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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 | 0.046 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.005 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".