Oral history interview with Dorothy Ahlswede Baker, 2007
Bibliographic record
Abstract
Primarily documents Dorothy A. Baker's nurse's training and service with the Army Nurse Corps from 1945 to 1947. Baker describes her childhood, including her German ancestry and being raised by her grandparents in Wisconsin. She discusses her nurse training through Bellin College in Green Bay, and mentions joining the Cadet Nurse Corps for the money. Other pre-service topics include working at the Sturgeon Bay Hospital, her second job as a waitress on base, and being turned down for entry into the navy. " Baker primarily discusses her service with the Army Nurse Corps, including her reason for joining and family reactions. She describes basic training at Camp McCoy, Wisconsin, especially the weather, gas mask drills, and inadequate clothing. She notes the heat while stationed at Fort Jackson, South Carolina, but primarily focuses on her time aboard the USAHS Wisteria. Topics include nurses' duties on ships; German POWs; working with psychiatric patients; Christmas onboard the ship; and sightseeing in Bremerhaven, Germany, and New York City. " Baker describes her other assignments, including traveling with war brides on trains, helping with children onboard; visiting her siblings on these travels; requesting Pacific Theatre ship duty;" meeting and marrying her husband; their honeymoon trip to Alberta, Canada; and moving to North Carolina for him to continue his surgical career.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.156 | 0.033 |
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".