Bibliographic record
Abstract
Military euphemisms have been around for a long time. The current "collateral damage" is one used to describe civilian casualties resulting from military action. The terms "acceptable losses" and "acceptable damage" are euphemisms used to refer to a casualty rate that is deemed justifiable, in the view of the high command, by virtue of the nature of the objective. I have opted to use the more honest term "Acceptable Casualties" as my title. In the Great War, very high casualty rates were often accepted to achieve objectives of trivial importance. This book is dedicated to the memory of my father's generation; the Canadian men and women, long since departed, who served and fought in the Great War of 1914-1918. They covered themselves and their country in glory, but at a horrendous cost. They put Canada on the map and guaranteed her a place on the world stage. Out of a population of less than eight million, 450,000 answered the call to arms and served overseas and 64,976 of them were killed or died of their wounds. Another 150,000 were wounded but survived. Countless more, although they had no visible scars, carried psychological ones to their graves. Our memories of them are fading, replaced by those of more recent conflicts. They deserve to be remembered. Richard Philp
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 | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.009 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.030 | 0.007 |
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".