At home we have different mothers, but in the forest, we all have just one. —Cambodian Proverb Chubby
Bibliographic record
Abstract
farm/orphanage of a French-Khmer art historian, Monsieur Son Soubert.1 Son Soubert is the son of Son Sann, a prime minister from the 1960s, and his farm is about an hour from Phnom Penh in Kandal Province. Son Soubert tells me he has held many positions in the government throughout his life and, dur-ing Pol Pot, he was trained by sympathetic pied noirs to run a “superette ” in Nice, France. My own mother is a French-Algerian pied noir, whose family settled near Nice. On his farm the air is light, the countryside green, and Monsieur Soubert’s orphanage hopeful. There is a new orphan, the youngest child here, who has just arrived today. He is holding a skinny, yellow “doll,” and when my Cambodian-American friend and translator, Chath pierSath, asks the doll’s name, the boy says, “Chubby. ” When Chath asks the orphan his name he also says “Chubby. ” He shows us how he can touch his nose with his very pointy, cute pink tongue. We gather around the boy—I hesitate to call him “Chubby”—and his older brother. The two boys cling to each other. Chath, Son Soubert, and I are with a Polish-Canadian aide worker and her
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.016 | 0.008 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.034 | 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".