Beth Parks and Colonel Mary Cady, interviewed by Devida Kellogg
Bibliographic record
Abstract
Beth Parks and Lieutenant Colonel Mary Cady, interviewed by Devida Kellogg, August 25, 2002. Parks and Cady, on the Veterans Panel, speak of their experiences in the military during the Vietnam War era; reasons for enlisting in the military; society’s reactions to the Vietnam War; propaganda, including “The Green Beret” by Robert Moore and “The Ballad of the Green Beret” by Staff Sergeant Barry Saddler; Beth’s experiences in a MASH (Mobile Army Surgical Hospital); Beth’s participation in constructing an evacuation hospital; MK’s education and participation in the Army at the University of Kansas; the College Army Nurse and WAC Student Officer Programs; MK’s training at Fort McClellan, Alabama, and Fort Ben Harrison, Indiana; MK’s employment at Fort Riley, Kansas, and Fort Devens, Massachusetts; MK’s enlistment in the Army Reserves; MK’s retirement in April of 1990; their experiences as women in the military; sexism in the military; the G.I. Bill; Mr. Branneth, a Canadian Vietnam Veteran; opinions on women in combat; and education at the University of Maine. Text: no transcript. Recording: mfc_na3085_c2129_01 (C 2129). Time: 00:47:08. Photographs: p14552-p14557. Restrictions: None.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.080 | 0.011 |
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".