Bombsights and Adding Machines: Translating Wartime Technology Into Peacetime Sales
Bibliographic record
Abstract
On 10 February 1947, A.C. Buehler, the president of the Victor Adding Machine Company presented Norden Bombsight #4120 to the Smithsonian Institute. This sight was in service on board the Enola Gay when it dropped the first atomic bomb on Hiroshima. Through this public presentation, Buehler forever linked his company to the Norden Bombsight, the Enola Gay, and to history. Buehler’s ultimate goal, however, was the sale of adding machines, and while significant, the presentation to the Smithsonian was essentially the final step in a long running advertising campaign designed to sell adding machines. During the War, Victor was the Army’s main contractor for the production of Norden Bombsights. This work is an investigation into the dysfunctional relationship that existed between Victor Adding Machine Company, the Army, the Navy Bureau of Ordinance (BuOrd). Wartime shortages demanded that pre-war arrangements between the Army and BuOrd be reconsidered and it was agreed that the Army be allowed to build its own units. Within a year of production Victor sights were scrutinized for their inaccuracies, and ultimately Victor’s contract was cancelled ending the Army’s short sojourn into bombsight production.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.007 |
| Scholarly communication | 0.012 | 0.008 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.003 |
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".