Bibliographic record
Abstract
Abstract DAVID SOUTER WAS expected for lunch at the home of his Concord, New Hampshire, friends Ronald and Mary Ellen Snow. The Supreme Court’s fall term had not yet begun, and the justice regularly delayed his return to Washington as long as possible, leaving New Hampshire only a few days before the Court’s first conference in late September. But earlier that morning, September II, 2001, terrorists had launched their horrendous attacks on New York and Washington; Mary Ellen Snow seriously doubted their guest would appear. “Mary Ellen called me at the office that day, assuming that David wouldn’t be coming,” Ron Snow later recalled with a smile. “And I said, ‘You better have lunch ready, because he’ll show up.’ I went home at a quarter of twelve and at noon David drove into the yard. When we asked him how he got away from the [marshal at his Concord chambers], he said, ‘It was simple; I didn’t tell them I was going.’ He had gone to the florist’s and picked up a pot of flowers, and we sat on the side porch of our house. Mary Ellen put up a screen so no one would see him, and we had a long, lovely lunch.”
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.431 | 0.132 |
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".