Bibliographic record
Abstract
The Dog Speaks Michael Salcman (bio) —Interior with Dog by Matisse, 1934 I'm only half-asleep so I know you're standing therewondering if I'm asleep. Nope.It's not easy to rest under this table—for one thing, there's a strong downward slopeand gravity's got me half tipped out of my basketlike an apple by Cézanne.Talk about a flat world!For another, I can't get away from these colors,the red floor tiles, orange table legand pink wall burning on my lids like the sun.Then again I'm never alone; the kids think a gray dog is cuteand I'm the only dog in the room. I was bribed(that's my excuse) with a boneand a bowl of fresh water. Really,I wish you wouldn't stare—it's extra hard to be an iconwhen you're not an odalisque and have no hair.Here's the inside dope, he wore a vest when he painted thembut saved his housecoat for me. I liked sitting for him,he was never rude and spared me his violin.I think I look very dignified, not naked, just nude. [End Page 210] Michael Salcman Michael Salcman is a physician, brain scientist, and art critic. He served as chairman of neurosurgery at the University of Maryland and as president of the Contemporary Museum in Baltimore. His recent poems have appeared in New Letters, The Ontario Review, Harvard Review, Raritan, and New York Quarterly. His two most recent books are Stones in Our Pockets (Parallel Press) and The Clock Made of Confetti (Orchises Press). Copyright © 2010 Johns Hopkins University Press
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.009 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.148 | 0.052 |
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".