Bibliographic record
Abstract
In December 1915, as the First World War wore on, Acadian leaders meeting in New Brunswick deplored how soldiers from their communities were “lost in the crowd” of the Canadian Expeditionary Force. They successfully lobbied the federal government for the creation of an Acadian national unit that would be French-speaking, Catholic, and led by their own. More than a thousand Acadians from across the Maritime provinces, Quebec, and the American northeast answered the call. In Lost in the Crowd Gregory Kennedy draws on military archives, census records, newspapers, and soldiers’ letters to present a new kind of military history focusing on the experiences of Acadian soldiers and their families before, during, and after the war. He shows that Acadians were just as likely to enlist as their English-speaking counterparts across the Maritimes, though the backgrounds of the volunteers were quite different. Kennedy tackles controversial topics often missing from the previous historiography, such as underage recruits, desertion, and army discipline. With the help of the 1921 Canadian Census, he explores the factors that influenced post-war outcomes, both positive and negative, for soldiers, families, and communities. Lost in the Crowd offers a completely new and replicable approach to the traditional regimental history, reconstituting the lives of soldiers and their families. The focus on the Acadians, a francophone minority group in the Maritime provinces, significantly shifts our understanding of French Canada and the First World 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 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.035 | 0.010 |
| Scholarly communication | 0.011 | 0.009 |
| Open science | 0.001 | 0.011 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.050 | 0.008 |
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".