Invited Commentary: The Society for Epidemiologic Research’s Commitment to Diversity and Equity—Pathways to Filling the Glass
Bibliographic record
Abstract
In the article by Nobles et al. (Am J Epidemiol. 2021;190(9):1710-1720), characteristics of those epidemiologists selected for various chair and presentation roles at the annual meetings of the Society for Epidemiologic Research (SER) from 2015 through 2017 were examined. Characteristics that were compared included inferred gender, institutional affiliation, subject area, and h-index. Important disparities were observed between session chairs, speakers, and poster presenters. SER leadership considers diversity and equity to be priorities and is committed to positive change. New programs and processes have been used to broaden participation and improve diversity since 2018, but the SER must continue its efforts to change processes and monitor of the experiences of SER members. A diversity of perspectives within the SER membership and at its meetings will improve all aspects of our practice of epidemiology.
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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 | 0.011 | 0.068 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.052 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.010 |
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".