Bibliographic record
Abstract
WHEN I WAS STILL A TEENAGER, I developed a peculiar interest in the experiences of prisoners of war and, much to the amusement of my friends, turned the collection of POW lore into something of a hobby.Since then, hundreds of ex-prisoners have been generous with their time and assistance, and have graciously accepted my invitations to jot down their recollections on various subjects.For fostering a boyhood interest that eventually turned into serious research, my first debt must go to those old soldiers, many of whom were quite happy to help me again as this book took shape.They have been unstinting in their assistance, and have shown great understanding in allowing me to shuffle through their scrapbooks and pester them with seemingly endless questions; sadly some of them, including Percy Hampton, Gus Gruggen, and Dick McLaren, did not live to see the publication of this book.At the same time, I must thank various veterans' organizations that have provided assistance, both in locating personal papers and in tracking down ex-prisoners.Particularly helpful have been Cliff Chadderton (War Amputations of Canada), Lionel Hurd (Hong Kong Veterans' Association), Fred LeReverend and Bert Konig (National POW Association), and Bud Ward (RCAF Ex-POW Association).When this book was a doctoral dissertation at York University, it benefited greatly from the guidance and advice of my supervisor, Jack Granatstein, whose knowledge of the archival material allowed me to utilize many sources that I would otherwise have missed, and whose always cogent criticism prevented me from straying into the realm of
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.422 | 0.301 |
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; the direct Gemma label and the distilled Codex classifier 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".