Bibliographic record
Abstract
One of the most colorful and controversial figures in American intelligence, Herbert O. Yardley (1889-1958) gave America its best form of information, but his fame rests more on his indiscretions than on his achievements. In this highly readable biography, a premier historian of military intelligence tells Yardley’s story and evaluates his impact on the American intelligence community. Yardley established the nation’s first codebreaking agency in 1917, and his solutions helped the United States win a major diplomatic victory at the 1921 disarmament conference. But when his unit was closed in 1929 because gentlemen do not read each other’s mail,” Yardley wrote a best-selling memoir that introducedand disclosedcodemaking and codebreaking to the public. David Kahn de-scribes the vicissitudes of Yardley’s career, including his work in China and Canada, offers a capsule history of American intelligence up to World War I, and gives a short course in classical codes and ciphers. He debunks the accusations that the publication of Yardley’s book caused Japan to change its codes and ciphers and that Yardley traitorously sold his solutions to Japan. And he asserts that Yardley’s disclosures not only did not hurt but actually helped American codebreaking during World War II.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.483 | 0.407 |
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".