Bibliographic record
Abstract
“Not the slightest chance” was Winston Churchill’s April 1941 estimate of Hong Kong’s prospects in the face of a Japanese assault. When in December the attack came, his prediction proved sadly accurate in just 18 days of brutal and confused fighting. In this book, Tony Banham tells the story of the battle hour-by-hour, remarkably at the level of the individual participants. As he names individuals and describes their fates, he presents a uniquely human view of the fighting and gives a compelling sense of the chaos and cost of battle. More than 10% of Hong Kong's defenders were killed in battle; a further 20% died in captivity. Those who survived seldom spoke of their experiences. Many died young. The little ‘primary’ material surviving – written in POW camps or years after the events – is contradictory and muddled. Yet with just 14,000 defending the Colony, it was possible to write from the individual's point of view rather than that of the Big Battalions so favoured by God (according to Napoleon) and most historians. The book assembles a phase-by-phase, day-by-day, hour-by-hour, and death-by-death account of the battle. It considers the individual actions that made up the fighting, as well as the strategies and plans and the many controversies that arose. Not the Slightest Chance will be of interest to military historians, Hong Kong residents and visitors, and those in the UK, Canada, and elsewhere whose family members fought, or were interned, in Hong Kong during the war years.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.006 | 0.011 |
| Scholarly communication | 0.007 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.033 | 0.010 |
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".