Whose Knowledge Counts? Considering Gender, Sexuality, and Race in Description, Prediction, and Causal Inference in Oral Epidemiology
Bibliographic record
Abstract
This commentary examines how the systematic exclusion of voices across gender, sexuality, and race shapes oral epidemiology to serve the interests of a few powerful groups. Focusing on the core functions of the field, including description of disease patterns, prediction of outcomes, and causal inference, we elaborate on empirical examples to argue for a reorientation of oral epidemiology. We advocate for an approach that centers reflexivity, applies intersectional analyses, and embeds inclusive governance across all stages of knowledge production to advance more equitable oral health outcomes.
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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 | 0.152 | 0.331 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.007 | 0.065 |
| Scholarly communication | 0.013 | 0.023 |
| Open science | 0.006 | 0.007 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".