Whose Man in Havana? Adventures from the Far Side of Diplomacy - Audio Book
Bibliographic record
Abstract
In Whose Man in Havana? John Graham provides us with a direct look at international relations through his experience as a practitioner who, as he puts it, has been fortunate in his career within the Canadian foreign service and international organizations to be 'in the right place at the right time'. The stuff of novels, he never would have dreamed that his apprenticeship would have him stationed in Cuba spying for the CIA on Soviet military operations. Subsequent assignments proved to be as unexpectedly and bizarrely entertaining. Throughout the book, he has focused on the lighter side of people and places, but almost everywhere the dark side intrudes, particularly the man-made dark side, providing quite a bit of black comedy. He notes that diplomacy at its most effective is neither dry nor humourless. The book is focused mainly on Latin America and the Caribbean, but other chapters range across Bosnia, the UK, Ukraine, Kyrgyzstan, Palestine, and Crete.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".