Notice bibliographique
Résumé
He follows her into the clothing room. “Remember black, I just want black. Guys like me, we only wear black.” He is a thick-set man, shortish, with a rough-shaven chin. The cast on his foot thuds as he walks. He stands now in the doorway of the clothing room, scrutinizing the selection, hands on hips. “You know who I am, don't ya? Everyone knows me. I done it all, ya know. Viet Nam, Hell's Angels. All of it.” He coughs from deep in his chest, wipes his mouth with the back of his hand. “Here's some black trousers,” she says. He snatches them from her, moving into the room. He's already said no to socks and underwear. “Yep,” he continues, digging through the piles of clothes. “All of them cops with their big guns — they know me. Yep, and all them big doctors, they know me too. I'm famous. My whole family is.” He finds a woman's black blouse and stuffs it under his arm where he's holding the trousers. A lock of yellow-grey hair sweeps across his forehead. She finds a black leather jacket on a hanger at the back of the rack. He snatches it from her. “Might be a bit big,” she says. He grunts. “It'll look good on the street. I gotta look good on the street.” Under the arm again, with the blouse and trousers. There are no shoes to fit. “No bother,” he says, “only got but one good foot anyhow.” They leave the room together, his new clothes under his arm. She offers to go with him back to the floor. “You're wise not to trust me,” he says. “I've killed with my bare hands. Remember I been to Viet Nam.” He holds his hands up like trophies; they tremble slightly. Together they head off down the hall to the elevators. He's all smiles now, pleased about the new clothes and conversation. They get on the elevator; it's almost full with uniformed staff, visitors in business suits, and one patient — a young woman on a gurney. Everyone is staring straight ahead. His floor number has already been pushed. “Yeah, yeah,” he says. “You people treat me really well, you treat me real well here.” Everyone shuffles slightly. The young woman on the gurney smiles. She's the only one looking at him. “Ya know when I came in I had so much lice they had to give me three treatments. Can you believe it? Three treatments!” He grins. In one quick silent motion everyone backs away, pressing against the elevator walls, still not looking at him. He's beaming, standing in the newly opened space in the middle of the elevator. “Three treatments — and now they're all gone, every one o' them damned bugs.” The young woman on the gurney laughs softly. He chuckles, too, at the sight of everyone plastered against the elevator wall. Some are smiling now, just a little. “That's why I gotta have black clothes,” he says, showing the bundle to whoever cares to look. “'Cause black shows up the bugs the best.” The elevator stops, and he gets off with her. She sees him back to his bed. “Thanks,” he says, “stroking the black leather jacket. You treat me really good here. Real good.” Linda Clarke Artist in Residence Faculty of Medicine Dalhousie University Halifax, NS
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,001 | 0,001 |
| Études des sciences et des technologies | 0,006 | 0,001 |
| Communication savante | 0,006 | 0,006 |
| Science ouverte | 0,001 | 0,006 |
| Intégrité de la recherche | 0,003 | 0,004 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,811 | 0,507 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».