Bibliographic record
Abstract
The statement that I love writing more than just about anything else in the world will come as no revelation to my friends and family.It might, however, give some indication of the gratitude I feel, and the privilege it has been, for me to have been given the time and resources necessary to pursue a project of this scale.None of it would have been possible without a great deal of assistance.Work on Strangers in Arms began almost the same day that my first book, Canadians Under Fire, was published in 2009, proving that there is indeed no rest for the wicked.This book's life started at Queen's University, and it benefited immensely from the sharp eyes and minds of Dr Doug Delaney, Dr Ian McKay, Dr Lisa Dufraimont, and Dr Lee Windsor.Yvonne Place, the history graduate assistant at Queen's during my time there, was also with me every step of the way through the process.The debt of gratitude that I owe to Dr Allan English, without whom neither my first major project nor this one (nor, indeed, my next one) would have ever come to light, is one I will carry for the rest of my life.Many hands helped to build this particular house.Shen-wei Mark Lim of the Southern Alberta Institute of Technology was (once again) invaluable in helping me build the databases that allowed the tumult of my evidence to be organized into something resembling a meaningful form.Data on courts martial for sexual assault in the Canadian Army were provided by Dr Claire Cookson-Hills, based upon research she originally did for Dr Jonathan Fennell.Dr Feriel Kissoon carried out an extremely helpful reconnaissance-in-force at the National Archives in London on my behalf to fill gaps left by my initial research trip.Dr Matthew Trudgen and Dr Doug Delaney provided additional information, guidance, and
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.556 | 0.350 |
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".