Bibliographic record
Abstract
<JATS1:p>The defection of Igor Gouzenko in September 1945, more so than any other single event, alerted the West to the nature and scale of the Soviet espionage offensive being waged by the Kremlin. Apart from the dozen or so defendants convicted of spying, Gouzenko wrecked an organization that had taken years to develop, exposed the penetration of the Manhattan atomic weapons project, and demonstrated the very close relationship between the Canadian Communist Party and Moscow. Many credit this event as sparking the bitter but secretive struggle fought between the intelligence agencies of the East and West for nearly half a century.</JATS1:p> <JATS1:p>TheHistorical Dictionary of Cold War Counterintelligencetells the story of both sides' fierce efforts to penetrate and subvert the opponent while desperately trying to avoid a similar fate. Through a chronology, an introduction, appendixes, a bibliography, and hundreds of cross-referenced dictionary entries on the organizations, operations, events, and personalities that influenced counterintelligence during the Cold War, the world of double agents, spies, and moles is explained in the most comprehensive reference currently available.</JATS1:p>
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.001 |
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; both teacher heads agree on what is shown here.
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".