Bibliographic record
Abstract
wasn't the cold of modern technological society; it was the cold that comes from winter air, drifting through cracks in bare walls and uninsulated ceilings. The place was the attic of an old house; huge, with blackened beams coming to a point, unfinished wood floorboards, no heat, no insulation, no electricity. The rent was fifty dollars a month, with no utilities, for there were none. In a part of the attic separated from the rest by a makeshift wall of rattan hangings, a window looked out onto the quiet street, with a view above houses of fields, stretching into the distance. This was my bedroom. I worked a few nights each week at a pizza parlor, until three, sometimes four in the morning. I set up a cot for my bed, and strung extension cords up the stairs for lights, a space heater, and a hot plate. I got in the habit of staying up until nearly dawn every night, reading, drawing ink pictures on a little pad, smoking pot. During the days I went to the library, or tried natural foods recipes (yogurt made on a hot plate), or meditated. I meditated using the method of a yoga group I'd joined in college: Brah-ma, the first syllable in the inhale, the second on the exhale, focusing all the time on a spot in the middle of my forehead. Sometimes I would feel energy bursting out the top of my head, until I lifted and floated with the stars. Other times when I opened my eyes everything seemed vivid, rich and peaceful; the window
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.007 | 0.020 |
| Scholarly communication | 0.011 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.043 | 0.015 |
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".