Policy should change to improve invited speaker diversity and reflect trainee diversity
Bibliographic record
Abstract
Abstract The biomedical sciences have a problem retaining white women and underrepresented minorities in academia. Despite increases in the representation of these groups in faculty candidate pools, they are still underrepresented at the faculty level, particularly at the Full Professor level. The lack of diverse individuals at the Full Professor level contributes to the attrition of women and under-represented minorities, as it confirms unconscious biases. The presence of unconscious biases contribute to feelings of not belonging by trainees and are amplified by visual representation of who is presented as the “top scientist in their field”. Top scientists are not only defined by the attainment of Full Professorships, but also through invited seminar series. Invitations for faculty to present their research at other university departments is highly valued offer that provides an opportunity for collaborations and networking. However, if invited speakers do not represent the demographics of current trainees, these visual representations of successful scientists may contribute to decreased attitudes of self-identification as a scientist, ultimately resulting in trainees leaving the field or the academy. In this study, we compare invited-speaker demographics to the current trainee demographics in one microbiology and immunology department and find that trainees are not proportionally represented by speakers invited to the department. Our investigation prompted changes in policy for how invited speakers are selected in the future to invite a more diverse group of scientists. To facilitate this process, we developed a set of tips and a web-based resource that allows scientists, committees, and moderators to identify members of under-served groups. These resources can be easily adapted by other fields or sub-fields to promote inclusion and diversity at seminar series’, conferences, and colloquia.
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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 | 0.121 | 0.249 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.012 | 0.015 |
| Open science | 0.008 | 0.010 |
| Research integrity | 0.017 | 0.016 |
| Insufficient payload (model declined to judge) | 0.043 | 0.012 |
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".