Oral History Interview with Kenneth Harrell, January 21, 2007
Bibliographic record
Abstract
The National Museum of the Pacific War presents an oral interview with Kenneth Harrell. Harrell joined the Marine Corps in August 1942 and received basic training in San Diego. He was assigned to the 1st Marine Division as a radio operator with the 1st Amphibious Tractor Battalion and first encountered enemy fire during a practice landing on Goodenough Island. With Chesty Puller, he walked across Cape Gloucester amidst sniper fire. Harrell transferred to the 6th Amphibious Tractor Battalion, landing on Peleliu in the second wave. When his LV-2 Water Buffalo was hit twice in shallow water, Harrell’s shoes were blown off and he crawled ashore, shredding his hands and knees on the reef. After the air strip was secured, Harrell was put on shore patrol, deterring enemy barges. Holdouts remained in caves, and Harrell was bayoneted during a banzai charge. He killed his assailant and boarded a hospital ship. His battalion having been decimated, Harrell rejoined the 1st Amphibious Tractor Battalion at Okinawa, taking fragments when a nearby Japanese soldier committed suicide by grenade and hiding amidst urns in native burial grounds. In September 1945, Harrell transferred to the 1st Motor Transport Battalion in north China, facilitating the disarmament of Japanese tank units. He liberated French Canadian Marines and noted the sizeable population of white émigrés. Harrell returned home and was discharged in February 1946. He reenlisted during the Korean War.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.146 | 0.036 |
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".