Horrid Scenes and Marvellous Sights: The Citizen-Soldier and Sir Robert Ker Porter’s Spectacle of War
Bibliographic record
Abstract
The period of the French Revolutionary and Napoleonic Wars is now widely viewed as seeing the emergence of the first modern or ‘total’ war, as whole populations came to be mobilised for the nation’s war effort. The period’s print culture and popular entertainments responded to these demands by creating a media spectacle, eliciting popular support for the war by enabling its audience to visualise scenes of conflict. Sir Robert Ker Porter’s work as both a writer and artist had a significant presence in this emergent spectacle of war. Utilising personal correspondence from eyewitnesses and placing enormous emphasis in his work on accurate depictions of warfare, he sought to enable the citizen to share the soldier’s view of war, allowing him or her to visualise and imagine conflict from the vantage point of the soldier and his subjective experience. Whilst Porter thus sought to provide images of willing sacrifice for the nation, there is nonetheless a transgressive aspect to his work. By privileging the soldiers’ personal view, the images presented by Porter could conflict with the state’s attempts to control information about the war. The articulation of a soldier’s subjective experience of war could be unsettling, eliciting affective and horrified responses to war that were far removed from the needs of a militaristic state.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.023 | 0.024 |
| Scholarly communication | 0.013 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".