The Society for Epidemiologic Research and the Future of Diversity and Inclusion in Epidemiology
Bibliographic record
Abstract
"The mission of the Diversity and Inclusion Committee (D&I) in the Society for Epidemiologic Research is to foster the diversity of our membership and work towards the engagement of all members, from diverse backgrounds at all stages of their careers, in the Society's activities, with the intent of enhancing discovery in public health." As a foundational step in implementing our mission, the D&I Committee conducted a survey of SER membership. Here we report on the efforts we have undertaken to expand the diversity and inclusiveness of our Society and our aspirations for future efforts in support of D&I. Early on, we established the SERvisits program to conduct outreach to institutions and students that have historically been underrepresented at SER; we hope this program continues to grow in its reach and impact. We have also taken steps to increase the inclusiveness of SER activities, for example, by engaging members on issues of D&I through symposia and workshops at SER annual meetings and through social media. DeVilbiss et al. (Am J Epidemiol. 2020;189(10):998-1010) have demonstrated that there is substantial room for improvement with regards to diversity and inclusion within SER. We invite SER members to become involved and collaborate on this long-term goal.
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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 | 0.037 | 0.118 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.007 | 0.005 |
| Bibliometrics | 0.007 | 0.004 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.008 | 0.004 |
| Research integrity | 0.026 | 0.044 |
| Insufficient payload (model declined to judge) | 0.008 | 0.007 |
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".