A wave of women chemists: Mary Elvira Weeks and her University of Kansas colleagues
Bibliographic record
Abstract
YearsGottlieb was an accidental chemist.Starting out, she had no particular career in mind; she simply wanted to go to college.Arriving at the University of Kansas in 1918, the possibilities she found were, in her words, "like walking into a candy store I majored in everything my freshman year" (14).She was born on November 23, 1900, in Pleasanton, Kansas, south of Kansas City, the second of six children in the only Jewish family in town.Her immigrant parents worked at and later owned a dry goods store there.Her father "wanted us all to go to college," she said.Pleasanton High School, where Gottlieb was valedictorian in 1918, did not teach chemistry.At the University of Kansas, where she enrolled that fall, the course offerings were dazzling (15): I was so overwhelmed that I chose the one course that would take the longest time to conquer: an M.D.So I began to take prerequisites for the MD, such as bacteriology, chemistry, other sciences.But then I thought that with such
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 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 teacher head, 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".