A Sailor’s View of Early Service in the Marine nationale on the Eve of the First World War
Bibliographic record
Abstract
Conventional naval histories seldom consider the individual experiences of common sailors. Subaltern and other approaches to history from below use new sources to take a different viewpoint. In May 1913, recruit Georges Brucelle arrived in Toulon to start voluntary service in France’s Marine nationale. After completing common training, he specialized in torpedoes and undertook instruction to gain qualification. Assigned to a destroyer minelayer, Brucelle died along with many other of the ship’s crew during operations in 1915. Personal letters sent to his family reveal insights into the working and social lives of a French sailor just before the Great War. Les histoires navales conventionnelles tiennent rarement compte des expériences individuelles des marins ordinaires. Les approches subalternes et autres de l’histoire par le bas utilisent de nouvelles sources pour adopter un point de vue différent. En mai 1913, la recrue Georges Brucelle arrive à Toulon pour commencer le service volontaire dans la Marine nationale. Après avoir terminé une formation commune, il se spécialise dans les torpilles et entreprend une instruction pour obtenir une qualification. Affecté à un contre-torpilleur mouilleur de mines, Brucelle mourut avec de nombreux autres membres de l’équipage du navire lors d’opérations en 1915. Des lettres personnelles envoyées à sa famille donnent un aperçu de la vie professionnelle et sociale d’un marin français juste avant la Grande Guerre.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.026 | 0.022 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.005 | 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".