Bibliographic record
Abstract
Verdun is an unusual and fascinating place.Someone should set a novel thereand I'm not suggesting this just because I was born and raised in Verdun.But sometimes truth is more interesting than fiction.To really understand the local culture and grasp the nuances of daily life in that city, you almost have to be a Verdunite.To get a measure of Verdun in the early 1940s, the following imaginative exercise might help: take about 70,000 working-class people of almost exclusively British or French ethnic origin, confine them in a rectangular area of about six square kilometres, oblige them to live on top of each other in nearly identical two-and three-storey tenement flats where everyone knows everyone else's business, nearly surround the whole by a river and an aqueduct, and deprive the residents of a rail connection, an intercity bus depot, any hotels, nearly all forms of industry, or any licensed establishments.At this point you have created a rough uniformity of experience and an unmistakable sense of geographic distinctiveness.Finally, overlay the greatest military conflict the world has ever seen and observe what kind of responses and intracommunity dynamics develop.This is what this book is about.In 1990 or so I purchased a splendid book from a remainder table in a wellknown bookstore chain.Len Burrow and Émile Beaudoin's Unlucky Lady: The Life and Death of HMCS Athabaskan describes the operational history and eventual sinking in 1944 of one of the Royal Canadian Navy's most powerful fighting ships, the Tribal-class destroyer Athabaskan.At the end of the book the authors listed the names and hometowns of the ship's crew who had survived and those who had perished.To my surprise, there were five or six Verdunites.Growing up in Verdun in the 1960s and 1970s, I knew that there were many First and Second World War veterans residing in the community.I lived for eleven years on the street named for one of Canada's most famous wartime figures: George F. Beurling, the legendary fighter pilot, who was from Verdun.But it was in reading the names of a handful of Verdunites who had died in Canada's war that I was suddenly seized with the desire to learn more about my hometown's wartime history.What kind of place was Verdun in the 1940s, and who were the Verdunites?How many residents enlisted?How many became casualties?How did the community participate in the war effort, and how did it respond to the consequent loss of life?Did the war change Verdun?Within a year, I had quit my job as a technical editor with a defence contractor and enrolled in the doctoral program in history at McGill University.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.689 | 0.489 |
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".