Bibliographic record
Abstract
This dissertation is a combined digital history-narrative history project. It takes advantage of newly digitized historical newspapers and soldier files to explore how the people of Victoria B.C. Canada, over 8000 kilometers from the front, experienced the Great War 1914-1918. Although that experience was similar to other Canadian cities in many ways, in other respects it was quite different. Victoria’s geographical location on the very fringe of the Empire sets it apart. Demographic and ethnic differences from the rest of Canada and a very different history of indigenous-settler relations had a dramatic effect on who went to war, who resisted and how war was commemorated in Victoria. This study of Victoria will also provide an opportunity to examine several important thematic areas that may impact the broader understanding of Canada in the Great War not covered in earlier works. These themes include the recruiting of under-age soldiers, the response to the naval threat in the Pacific, resistance by indigenous peoples, and the highly effective response to the threat of influenza at the end of the war. As the project manager for the City Goes to War web-site, I directed the development of an extensive on-line archive of supporting documents and articles about Victoria during the Great War that supports this work (http://acitygoestowar.ca/). Once reviewed by the committee, this paper will be converted to web format and added to that project.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".