Bibliographic record
Abstract
Population shifts in the workforce have been noted for the past few decades. In the United States, the number of people aged 65 and older is expected to double, reaching almost a quarter of the population.1 By 2045, the US is expected to experience a demographic shift, with an increase in the percentage of minority populations to greater than 50%. This diversity is especially noted in younger age groups and is accompanied by an increase in the number of women earning professional degrees at the undergraduate and graduate levels.2,3 However, despite this progress, women in the US continue to experience a significant pay gap, earning approximately 82 cents for every dollar earned by men.4 Within the US Rheumatology clinical community, it is estimated that on average, for every dollar a male rheumatologist earns, a female rheumatologist earns 83 cents.5 This represents a 17% difference in compensation (average 2016–2018), translating to a significant increase in the number of years needed to work to reach earnings parity. In extrapolating these numbers over a 40-year period, the difference in mean salary between the higher earning male rheumatologist to the average female rheumatologist is $1,760,000. The American College of Physicians, in its position paper in 2018 on gender equity in physician compensation and career advancement, noted … Address correspondence to Dr. G.C. Wright, 345 E 37th Street, Suite 303C, New York, NY 10016, USA. Email: GCWright.MD{at}gmail.com.
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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 | 0.007 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.036 | 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".