A new innovative method to measure the demographic representation of scientists via Google Scholar
Bibliographic record
Abstract
Many countries around the globe have seen increases in the enrollment of female and visible minorities in postsecondary education. Therefore, it is critical to evaluate whether recent demographic changes at the postsecondary institution have translated to employment opportunities in scientific fields for women and previously underrepresented groups. Instead of relying on algorithm indices, surveys, or anonymous census data, this study is the first research to utilize an innovative approach to report the demographic representation of top-ranking scientists from around the world. The recently developed Google Scholar profile platform, university ranking system, and the search engine are the main methods that allowed this study to identify and categorize the top scientists from countries in which English is one of the official languages, or where English is used as the language of instruction in higher education. Overall, findings reveal that at top-ranking universities in which the majority of the population is Caucasian, women and minorities are severely underrepresented in all areas of science, capturing 7.3% and 6.4% of the total citations, respectively. Each country’s highest concentration of scientists in each field, based on citation and percentage of researchers, is highlighted. There are recommendations offered to help make scientific advancement more favorable to underrepresented groups, and also to encourage institutions of higher education to adapt and build new capacities.
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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 | 0.007 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.033 | 0.048 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".