How to do cities with words. Ville, espace et littérature à l’ère hyperconnectée
Bibliographic record
Abstract
How to do cities with words. Ville, espace et littérature à l'ère hyperconnectéeVivre, c'est passer d'un espace à un autre en essayant le plus possible de ne pas se cogner.Georges Perec, Espèces d'espaces Je tiens à remercier la Chaire de recherche du Canada sur les écritures numériques, son titulaire, Marcello Vitali-Rosati, et l'équipe entière pour le soutien et les échanges qui nourrissent ma pensée, chaque jour.5 Pour une perspective plus actuelle sur la géolocalisation et les arts, je renvoi au site internet du GPS Museum : http://gpsmuseum.eu/6 C'est dans le domaine des sciences de l'information et de la communication que l'on trouve la plupart des chercheurs et des chercheuses qui se penchent sur notre rapport à l'espace à l'ère du numérique : outre Nicolas Nova et Boris Beaude, Laurence Allard notamment est une des chercheuses les plus impliquées dans ce domaine.Pour un approfondissement de son travail, je renvoi à son blogue sur la culture mobile : http://www.mobactu.org/7 [Insérer ici des images de projets artistiques, VERSION AUGMENTÉE]
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.008 | 0.013 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.052 | 0.025 |
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".