Bibliographic record
Abstract
OF A LOVED ONE may evoke anguish, regret, confusion, am ger, shock, bitterness, despair, relief, gratitude, nostalgia, even joy.But the death of my friend Bonnie evoked in me, both on that Friday morning in September of 2001 and now, three years later, wonder.Her remarkable life began in New Orleans on June 19, 1948, when a young and single Canadian woman bore an infant she would give to Niona and Bertram Bobet, a childless, older couple from Oakland, California.Defying her destiny as an only, adopted child, Bonnie would repudiate the adjectives "only" and "alone" and would make herself the center of an enormous family.Even as a child, Bonnie made sure she rarely had the back of the family car to herself.Her cousin Connie spent most weekends and vacations with the Bobets, and the two regarded themselves as sisters.After Bonnie learned to drive, they shared the front seat of the car as well; when they were sure that Niona and Bertram were asleep, they would sneak over to the garage, release the car's brake, roll it down to the street, and go for a clandestine spin.Later, when Connie would become a too-young wife and mother, Bonnie became a doting aunt to little Richard; and to give Connie some time off she would wrap him in blankets, lay him on the floor of the car (this was before mandatory seat belts and car seats), and take him home with her for a good session of spoiling.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.030 | 0.009 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.004 | 0.010 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".