Bibliographic record
Abstract
The one he did, that is, and took me to when I was 13. I turned as white as the old woman lying naked there; but as he clanked out tools I inspected her quickly, the dead cinder of her nipples, the stiff tuft at her crotch (“Still black? Wouldn’t it turn gray?”). Dad took stock of her length, weight, muscle tone, telling me or the microphone how she lived, what made her sick. “Like being a detective,” he said, “except I answer my own questions. Here; touch this.” But I wouldn’t, and I wanted her body to resist interrogation, prayed weirdly she never said “aah” for a doctor. Then he slit and sawed her down the middle— she opened as easily as a yam, or a duffel bag; dipping delicately in, Dad scooped out a handful of stuff, all jumbly like underwear from Mom’s dresser. He read her guts like a priest: proving the tubes, slicing wafers from her heart, so thin they would glow under lens-light— at last she yielded him a brown pebble which I felt between his finger and thumb; then he put it back. Death’s story, deduced from facts hard as bone—as he talked me through it, I could hear the joyful lift in his voice.… He had little patience for his house, its prattling unready anatomies, his wife’s “incompetent housekeeping”; at night he sat over journals and drinks, compact, severe, inward as a microscope. Now he’s home all day waiting for the mail, hasn’t cut a corpse for years. He calls every weekend, his news familiar as a backache, and we talk without fear. Once I thought my pen would open him here like the corpse on its single pan of judgment; but as I cover this pan with pages he is alive on another one.
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".