Bibliographic record
Abstract
Que reste-t-il d’une bataille sur le lieu où elle s’est déroulée ? Peu de choses assurément, même si l’empreinte spatiale des grands combats de l’ère industrielle est plus évidente, des dégâts environnementaux aux grands monuments postérieurs. Pourtant, les sites de combat de toutes les époques suscitent de l’intérêt, de la curiosité, voire un véritable engouement. Un tourisme spécifique s’est développé, dont les racines remontent loin. Aux yeux de ces visiteurs, les champs de bataille portent une part irréductible de la mémoire des guerres et celle-ci s’y inscrit d’une manière de plus en plus massive à travers de multiples usages sociaux, culturels, politiques, pédagogiques. Dans ce volume, une vingtaine de chercheuses et chercheurs explorent cette histoire des lieux de mémoire, du xvie siècle à nos jours. Leurs articles font varier les échelles et les démarches, et transporteront le lecteur des guerres d’Italie à la guerre du Pacifique, et du Québec à la Lorraine, en passant par le Portugal ou la Normandie.
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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.009 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.004 | 0.008 |
| Insufficient payload (model declined to judge) | 0.035 | 0.012 |
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".