Distributors, Agents, and Publishers: Part II: The Toronto Publishing Scene During World War I
Bibliographic record
Abstract
George Parker"The war seems to have u~ncovered a new Canadian reading public just as much as it has broulght new writers to birth, and with the improved machinery ofpublishing there never waEs a more appropriate movement [sic] for a new Canadian literature.-J.M. Gibbon "Whiere is Canadian Literature?" (1918)3 Between 1914 and 1918 the country changed dramatically, although its population of nine million hardly grew.Of the 628,000 Canadians who enlisted in the military, about 425,ooo went overseas.In the famous battles of Ypres, Passchendaele, and Vimy Ridge, the Canadians were gassed, shelled, frozen, and panic-stricken in their mud-filled trenches.We~ can read of these events in first-hand accounts by professional and amateur writers: in Robert Service's Rhymes ofa Red-Cross Man (I916), which describes the poet's experiences as an ambulance driver, or in the responses of Bernard Trotter, who was killed in action before the publication of his volume, A Canadian Twilight, and Other Poems ofWar and ofPl~eace (I917), or in I9-year-old Edgar McInnis's poetry pamphlets, or in Frank Prewitt's modernist volume, P)oems (192I).The middle-aged Charles G.D. Roberts and Charles Gordon made it to the front lines but, like Gilbert Parker, they were soon recruited for publicity and propaganda work.
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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.015 |
| Science and technology studies | 0.042 | 0.012 |
| Scholarly communication | 0.022 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.042 | 0.003 |
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".