Panser les blessures en soignant la mémoire : stratégies de mise en espace des lieux de mémoire d’atrocités
Bibliographic record
Abstract
Cet article étudie le rôle de l’architecture dans l’inscription de la mémoire collective des atrocités de masse dans le paysage urbain. À travers l’analyse d’espaces publics mémoriels situés dans quatre villes européennes, il explore les façons dont l’architecture peut agir comme un langage non verbal apte à traduire, sous une forme matérielle, une réalité trop dure pour être communiquée autrement. Nous soutenons que les qualités esthétiques du mémorial, la relation au site, l’organisation spatiale, le chemin de circulation ainsi que l’utilisation spécifique des matériaux, des textures et des symboles créent un environnement propice à la réceptivité, à l’empathie et à l’introspection. L’article suggère que l’architecture mémorielle peut avoir un effet positif sur les sociétés urbaines, dans le cadre d’un mouvement vers la guérison collective, la réparation historique et la diminution des inégalités sociales.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.015 | 0.020 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.009 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".