Bibliographic record
Abstract
A PATRIOTIC CANADIAN, a dutiful daughter, a devout Anglican, a loving sister, a dear friend, an adventurer, a romantic, -and a nurse.Indeed, only as a nurse could she, a woman, go to war at all.Two thousand other women preceded, accompanied, or followed her overseas between 1914 and 1918, nursing sisters with the rank of lieutenant in the Canadian Army Medical Corps (CAMC).A varying number, between four hundred and eight hundred -Clare Gass among them -saw service in France, and an even smaller number -Gass there too -served close to the line of fire.All expected to be home soon, and home alive.The war belied the first expectation: in Clare Gass' case her time in Europe stretched to almost four years.The second expectation she and most of her colleagues did achieve.Thirty-nine did not.The experience of the military nurses and the aura surrounding them contributed in large part to the quadrupling of the number of nurses in Canada between 1911 and 1921.l Nothing in Clare Gass's background or upbringing suggested a career at all, much less one in military nursing.Born in Nova Scotia in 1887 to small-town, middle-class parents of Scottish origin, Clare was the oldest living child and only girl in a family of ten, three of whom did not survive infancy or childhood.Her father, Robert, owned a general store and lumber mill in Shubenacadie; her mother, Nerissa, came from the Miller brickmaking family just across the river.Clare and her father were kindred spirits -quiet, c Clare reached adolescence and outgrew the local school, her parents chose Edgehill, a private Church of England school for girls in Windsor, Nova Scotia, for her secondary schooling.In 1901, when she went as a boarder, Edgehill was only ten years old but was already Gass family, Shubenacadie, late nineteenth century.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.555 | 0.370 |
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".