Are professors of human nutrition faculty at Canadian universities representative with respect to common social constructs of gender and race?
Bibliographic record
Abstract
Attributes such as sex and race/ethnicity are associated with inequities in representation. The objective of this study was to assess representation of 2 social constructs, gender and race, of professors of human nutrition in Canada. Using information publicly available October 2021, individuals with the title of assistant, associate, or full professor were identified on websites of 20 Canadian universities offering undergraduate and/or graduate degrees in human nutrition. Individuals were subjectively stratified to social constructs, i.e., white, racialized, or Indigenous, based on photographs, ethnic origin of a surname, and regional and ethnic origin disclosures on university websites, LinkedIn, social media, etc. Gender was assigned based on publicly available photographs and self-disclosed pronouns (when available). Of the 190 individuals, 80% were white, 16.4% were racialized, and 2.6% were Indigenous peoples. The majority (65.3%) were women. In a subset with established doctoral thesis dates and dates of hire at their current institution (n = 153), racialized and Indigenous professors, especially assistant and associate, had earned their doctorate and been hired more recently than their white peers. This study is limited because only individuals with professorial titles were included and the assignment of social constructs for race and gender was subjective. Nevertheless, it establishes an understanding of the proportions of professors of human nutrition who are white, racialized, Indigenous, women, and men. Novelty: Canadian universities strive to be equitable, diverse, and inclusive. One hundred and ninety professors of human nutrition were stratified using social constructs for race and gender. Findings: 65% Women, 80% white, 16.4% racialized, and 2.6% Indigenous
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".