Oral History Interview with James Black
Bibliographic record
Abstract
The National Museum of the Pacific War presents an oral monologue with James Black. Black was born in Glasgow, Scotland on 10 May 1920 and joined the Territorial Army in 1937. In August 1939 his unit was mobilized when Great Britain declared war on Germany. He served in Belgium and France and was evacuated at Dunkirk in May 1940. In 1941, he volunteered to be an instructor in the Indian Army. In March 1942 he went to Roorkee, India where he was assigned to King George the First Bengal Sappers and Miners (Indian Engineers). As a trainer of drivers, mechanics and machinists, he worked with Hindus, Sikhs and Muslims and learned to speak Hindustani. In February 1943, he was assigned to the 16th Field Company of the regimental headquarters and sent to Bombay. He recalls a horrific explosion and fire that occurred on 14 April 1944, which caused the death of hundreds. In May 1944 Black’s became part of the 33rd Indian Corps and he describes the deplorable conditions encountered while fighting to break the Japanese siege of Imphal, India. He recounts building Bailey bridge over the Chindwin River in December 1944 on the way to Rangoon. In July 1945, his unit boarded ships to participate in the invasion of Malaya. While on board, they received notice of the surrender of Japanese forces. In December 1945 he arrived in England and recalls being met at the dock by his brother who had been wounded in France. He concludes the narrative by telling of migrating to Canada and ultimately moving to California.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.020 | 0.003 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.059 | 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".