Bibliographic record
Abstract
Cet article examine une vingtaine de projets ayant consisté à créer des poèmes in situ dans les villes, entre 1995 et 2020, aux Pays-Bas où Leyde a joué un rôle pionnier, en Allemagne, en France, en Bulgarie, aux États-Unis, au Canada et au Royaume-Uni. À travers le monde, des villes de plus en plus nombreuses accueillent en effet des poèmes sur leurs murs, sur leur sol, et même sur le mobilier urbain, comme les bancs. On confie à ces inscriptions la tâche de réenchanter l’espace quotidien – et il y a là un véritable trait d’époque. Il s’agit ici d’observer comment divers supports sont investis et déclinés : la pierre ou la brique gravées, les façades peintes, la chaussée où la pluie écrit grâce à des pochoirs, mais aussi et surtout le site qui accueille le poème. Nous tentons de comprendre comment procèdent ces textes, qui le plus souvent ne se veulent pas pérennes, et comment la lecture se transforme dès lors qu’elle est située.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.009 | 0.012 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.026 | 0.003 |
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".