Bibliographic record
Abstract
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
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.811 | 0.507 |
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".