Bibliographic record
Abstract
By the summer of 1917, Canadian troops had captured Vimy Ridge, but Allied offensives had stalled across many fronts of the Great War. To help break the stalemate of trench warfare, the Canadian Corps commander, Lieutenant-General Arthur Currie, was tasked with capturing Hill 70, a German stronghold near the French town of Lens. After securing the hill on 15 August, Canadian soldiers endured days of shelling, machine-gun fire, and poison gas as they repelled relentless enemy counterattacks. Through Their Eyes depicts this remarkable but costly victory in a unique way. With full-colour graphic artwork and detailed illustration, Matthew Barrett and Robert Engen picture the battle from different perspectives – Currie’s strategic view at high command, a junior officer’s experience at the platoon level, and the vantage points of many lesser-known Canadian soldiers who made the ultimate sacrifice. This innovative graphic history invites readers to reimagine the First World War through the eyes of those who lived it and to think more deeply about how we visualize and remember the past. Combining outstanding original art and thought-provoking commentary, Through Their Eyes uncovers the fascinating stories behind this battle while creatively expanding the ways that history is shared and represented.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.011 | 0.011 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.107 | 0.044 |
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".