Epidemiologists Count: The Role of Diversity and Inclusion in the Field of Epidemiology
Bibliographic record
Abstract
We present interpretations of the idea that "epidemiologists count" in response to the current status of membership and diversity and inclusion efforts within the Society for Epidemiological Research (SER). We review whom epidemiologists count to describe the (mis)representation of SER membership and how categorizations of people reflect social constructions of identity and biases that exist in broader society. We argue that what epidemiologists count-how diversity and inclusion are operationalized-has real-world implications on institutional norms and how inclusive/non-inclusive environments are. Finally, we examine which epidemiologists count within the field and argue that inclusion can only be achieved when we address how resources and opportunities are distributed among epidemiologists. To improve diversity and inclusion within SER and beyond, we recommend that SER strengthen its commitment to diversity, inclusion, and equity by: 1) integrating this priority on all agendas; 2) enhancing efforts to improve self-awareness among members and accountability within the organization; 3) supporting the growth of a diversifying workforce in epidemiology; and 4) increasing the visibility of health disparities research and researchers in 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 | 0.032 | 0.091 |
| Meta-epidemiology (narrow) | 0.004 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.007 | 0.011 |
| Scholarly communication | 0.018 | 0.012 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.019 | 0.036 |
| Insufficient payload (model declined to judge) | 0.005 | 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".