Introduction to a facsimile edition of Smith, Lorenzo N., Lingo of No Mans Land : A World War 1 Slang Dictionary.
Bibliographic record
Abstract
E.W.’s foreword to Lingo of No Man’s Land describes its author as ‘a typical son of Massachusetts… fired by the newspaper reports of desecrated France and Belgium’. The foreword explains that he became so impatient with US reluctance to declare war that he crossed the border into Canada to enlist, serving for a year in the Westmount Rifles before a shrapnel injury brought his active military career to an end in Messines in April 1916. Lorenzo Napoleon Smith was indeed born in Massachusetts, in a town called Lowell, but he had returned to Canada with his Canadian family by the time of the 1911 census, when they were living in Montreal. On the 16th of February 1915, Smith left his job as an electrician to join the 23rd Battalion of the Canadian Expeditionary Force, giving Montreal as his place of birth (indicating that he considered himself fully Canadian despite being born in the US). He was transferred to the 4th Battalion on the same day and sailed from Halifax on the SS Missanabie six days later. After a short period in England, Smith arrived in France on the 7th of May and joined his Battalion in the reserve trenches at Festubert on the 23rd. Several days of heavy shellfire and numerous casualties must have provided a shocking introduction to the realities of trench warfare. [Extract from opening paragraph]
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) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.229 | 0.168 |
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".