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Record W4390736157 · doi:10.1017/jbr.2023.8

“Lavender for Lads”: Smell and Nationalism in the Great War

2023· article· en· W4390736157 on OpenAlexafffund
Jessica P. Clark

Bibliographic record

VenueJournal of British Studies · 2023
Typearticle
Languageen
FieldArts and Humanities
TopicHistorical Art and Culture Studies
Canadian institutionsBrock University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWhite (mutation)Front (military)Spanish Civil WarNationalismPower (physics)HistoryLavenderOrder (exchange)SociologyAdvertisingMedia studiesLawPolitical scienceEngineeringArchaeologyBusinessPolitics

Abstract

fetched live from OpenAlex

Abstract In the Great War, home front schemes in support of wartime causes included the making and transportation of what were called smellies : homemade tokens and commercial gifts that invoked supposedly traditional British scents. For volunteers, this entailed the collection and distribution of homemade lavender and verbena bags as an allegedly effective—and practical—means of aiding those injured at the front. For others, like commercial perfumers, this meant the production of scented commodities like lavender water and eau de Cologne for transport to troops overseas. In both cases, supporters mobilized the symbolic power of perfumed items to promote a fictitious version of rural, white, English life that could allegedly be resumed after the conflict. These campaigns obscured the social, racial, gendered, and material realities of war. What resulted was a profoundly limited definition of British smells and, by extension, their idealized British recipients: white, English-born servicemen from across classes. While perfumed gifts were designed to comfort these select recipients and bring a sense of order to the front, accounts of gifts’ production and reception ultimately reveal fractures—and failures—in the deployment of national smells to order the disordered smellscapes of war.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.356
Threshold uncertainty score0.278

Codex and Gemma teacher scores by category

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

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 head0.077
GPT teacher head0.285
Teacher spread0.208 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreEmpirical

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
Published2023
Admission routes2
Has abstractyes

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