Bibliographic record
Abstract
"[Marshall's] work in responding to the challenge of exploring a little-known life should be an inspiration to other students of history … people across Canada will find it a pleasant way to become better acquainted with an attractive, interesting and unfamiliar contributor to our history." - Desmond Morton, McGill University Give Your Other Vote to the Sister tells the story of Roberta MacAdams, the first woman elected to the Alberta legislature. In fact, she was one of the first two women elected to a legislature anywhere in the British Empire. Her triumph was extraordinary for many reasons. Not only did she run while serving as a nursing sister overseas during the Great War, but over 90 per cent of her electors were men - Alberta soldiers stationed in England and in the muddy trenches of the Western Front. Give Your Other Vote to the Sister describes MacAdams' journey overseas, her work at a large military hospital in London, and the personal sacrifices she endured during the war. It also chronicles Debbie Marshall's own journey to reclaim MacAdams' life, one that took her across Canada and to the places where MacAdams lived and worked in England and France. It was a search that would change her own perceptions about how and why so may women willingly participated in the world's first "great war."
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.361 | 0.125 |
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".