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Record W2583787480

Introduction to a facsimile edition of Smith, Lorenzo N., Lingo of No Mans Land : A World War 1 Slang Dictionary.

2014· book-chapter· en· W2583787480 on OpenAlexaboutno aff
Julie Coleman

Bibliographic record

VenueLeicester Research Archive (University of Leicester) · 2014
Typebook-chapter
Languageen
FieldArts and Humanities
TopicLinguistics, Language Diversity, and Identity
Canadian institutionsnot available
Fundersnot available
KeywordsFacsimileSlangHistoryLinguisticsClassicsArt historyLibrary scienceCartographyArtGeographyComputer sciencePhilosophyTelecommunications
DOInot available

Abstract

fetched live from OpenAlex

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.401
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.041
GPT teacher head0.243
Teacher spread0.202 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designNot applicable
Domainnot available
GenreOther

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".

Quick stats

Citations0
Published2014
Admission routes1
Has abstractyes

Explore more

Same venueLeicester Research Archive (University of Leicester)Same topicLinguistics, Language Diversity, and IdentityFrench-language works237,207