Who is My Donor? The Local Propaganda Techniques of London's Emergency Blood Transfusion Service, 1939-45
Bibliographic record
Abstract
This article contributes to the history of propaganda during the Second World War by turning away from the celebrated innovations of the wireless, cinema, and printed press, and examining the small-scale recruitment techniques developed by London’s Emergency Blood Transfusion Service (EBTS). In order to recruit and retain civilian volunteers, organizers of the EBTS subordinated methods of ‘national publicity’ to the emergent techniques of ‘local propaganda’. The local recruitment drive, the scheduled mobilization of local authorities and venues, and the face-to-face provision of advice became the preferred strategies for attracting new members. At specialist blood depots and other bleeding venues, such details as the arrangement of beds and screens, the provision of hot tea, and the demeanour of nursing staff all counted among persuasive practices used to reassure volunteers. Noting the correlation of these phenomena with the expansion and industrialization of the British blood supply, which entailed the complete dissolution of the one-to-one relation between donors and recipients, this article situates a new emphasis on locality and familiarity as part of a broader reaction against the combined spectres of total warfare and modern medicine. In what is often framed as the golden age of mass communication and mass society, locality and familiarity became priorities for a large-scale emergency medical service.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.012 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.012 | 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".