Bibliographic record
Abstract
MODERATOR: Stacy L Christiansen JAMA SPEAKERS: Stacy L Christiansen Emily L Ayubi American Psychological Association Sabrina J Ashwell Chemical & Engineering News American Chemical Society Leonard Jack, Jr Preventing Chronic Disease Journal CDC REPORTER: Michele Springer Caudex Incorporating inclusive language into scientific communications helps establish respect for all people and promote inclusion. Without inclusive language, communications can perpetuate bias based on personal characteristics, background, and stereotypes. The purpose of this session was to share examples of how different organizations are incorporating inclusive language into their style guides to improve inclusivity across all communication. Stacy Christiansen opened by providing examples of how the AMA Manual of Style is incorporating inclusive language guidance. In addition to being Managing Editor for JAMA, Stacy is the Chair of the AMA Manual of Style Committee. The 9th edition of the AMA Manual of Style, published in 1988, was the first edition to provide examples of inclusive language terms, policies, and guidance. Since then, it has been updated multiple times, with the most recent updates on race and ethnicity guidance added in August 2021.1,2 Currently, the Committee is updating the sections on sex, gender, and sexual orientation. Guidance on language used to discuss age, socioeconomic status, and abilities, disabilities, conditions, and diseases will be updated in turn. Current guidance for reporting on sex and gender includes the following: “Sex” should be used when reporting biological factors; “gender” should be used when reporting gender identity or psychosocial/cultural factors. Explain methods used to obtain information on sex, gender, […]
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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 | 0.050 | 0.236 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.005 | 0.005 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.007 | 0.013 |
| Insufficient payload (model declined to judge) | 0.164 | 0.144 |
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".