âMy Darlinâ Clementine?â Wooing Zombies for $6.50 a Night: General Service-NRMA Relations in Wartime Calgary
Bibliographic record
Abstract
T he 18th of July, 1944 was another bloody day in a long and bloody war.When night's cool shadows crept stealthily across the battlefields of Europe, the Americans' XIX Corps had entered St. Lô, the British had launched Operation Goodwood around Caen, and the Canadian II Corps had crossed the Orne river doing their 'bit' in Operation Atlantic.In Italy, Allied units were on the move across the peninsula, reaching the outskirts of Leghorn in one area, the Arno river at Pontedera in another, while the Polish II Corps, slowly but surely regaining national honour, had captured Ancona.The Russians were also on the march, advancing east of Lvov in Poland and towards Lublin to the north.They were nearing East Prussia as well, despite the best efforts of Model and the still brilliant German panzer generals.And in the far off Pacific, the government of General Hideki Tojo had fallen, just as the Americans closed in on Aitape in the stifling, inhospitable wilds of New Guinea.And in Germany too, amidst the ceaseless rain of high explosives and incendiaries pouring from the bellies of Allied bombers, a bold if tardy group of conspirators put the finishing touches on their plan to assassinate Der Fuhrer, a plot executed unsuccessfully, by a narrow margin, but two days later.Across the length and breadth of the war fronts on July 18, in other
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.016 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.006 | 0.009 |
| Insufficient payload (model declined to judge) | 0.272 | 0.048 |
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".