Bibliographic record
Abstract
Bolshevizing Britain in the Interwar Imagination interprets the emergence of anti-Bolshevik popular fiction (also known as Red fiction) in Britain as manifesting a range of responses to the perceived "lessons" of the Great War. It argues that these responses foreground two opposing interpretations of the Great War in Britain: on the one hand, a militarist interpretation justifying the use of military power to quell labour unrest in Britain after the war; and on the other, an anti-militarist argument advocating for the preservation of peace as the only course of action to ensure Britain's long-term survival. Examining the novels of four British popular fiction writers (H. C. McNeile's Bull-Dog Drummond (1920) and The Black Gang (1922), Emerson C. Hambrook's The Red-Tomorrow (1920), Edward Shanks's The People of the Ruins (1920), and J. D. Beresford's Revolution (1921)), this dissertation identifies the figure of the ex-serviceman in this body of literature as a focal point for discussions of Bolshevism and revolution. In their depiction of ex-servicemen, these novels adapt conventions of archetypal romantic heroism to suit a period wherein heroes were imagined as returning from rather than leaving for war. Bolshevizing Britain moves away from assessments of early interwar British anti-Bolshevik fiction as a static set of conventions, reconsidering this body of work for the wealth of information it offers on conservative responses to labour unrest in interwar Britain. In the years shortly after the Bolshevik Revolution (1917), the possibility of an English revolution modelled on the Russian revolutionary context was still plausible. This possibility made one of the central questions of the time in Britain—"How to address labour and political unrest in the United Kingdom?"—inseparable from cultural debates over the long-term sociopolitical consequences of employing military measures to ensure British hegemony. Bolshevizing Britain shows how the anti-Bolshevism of these novels emerges precisely at the point of contact between the authors' portrayals of ex-servicemen as vessels for ideological contributions to these cultural debates over militarism, and the authors' imaginative reinvention of prewar generic conventions to fit the cultural mood of early interwar Britain.
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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.033 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".