The roosting box rebuilding the body after the First World War
Bibliographic record
Abstract
"At the end of the First World War, a cash register factory in Toronto was renovated to treat wounded soldiers returning home. From 1919 to the 1940s, thousands of veterans passed through its doors. Some spent the remainder of their lives there. An exquisitely written history of the early years of the Christie Street Hospital (also known as the Dominion Orthopedic Hospital) and how war reshaped Canadian society, The Roosting Box places ordinary people at the heart of the story: veterans learning to live with their injuries and a world irrevocably changed; nurses caring for patients while carrying their own wartime trauma; and doctors pioneering research in prosthetics, plastic surgery, or a treatment for diabetes. Naming chapters after parts of the body, den Hartog chronicles injuries and treatments, and the struggles and accomplishments of patients and staff. The cast of characters is diverse--Black, female, Indigenous, and people with all sorts of physical and mental challenges--and their experiences, gleaned from diaries, letters, service records, genealogical research, and interviews with descendants, are surprising and illuminating. An unusual mix of history and story, The Roosting Box offers deeply personal perspectives on healing in the aftermath of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.036 | 0.025 |
| Scholarly communication | 0.008 | 0.003 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".