Bibliographic record
Abstract
In a small village in Anatolia, Nasreddin was known for his wit and wisdom. One day, the villagers approached him with a peculiar problem. "Nasreddin," they said, "we cannot seem to catch the thief who keeps stealing our chickens. We need your help!" Nasreddin thought for a moment and then declared, "Tomorrow, bring me a large pot of soup and everyone in the village." The next day, the villagers gathered with the pot of soup. Nasreddin stood before them and said, "I have devised a foolproof way to catch the thief. Each of you must dip your finger in this soup and taste it. The one whose finger tastes different will be our thief!" Puzzled but trusting the Hodja, the villagers did as instructed. After everyone had tasted the soup, Nasreddin pointed to one man and said, "This is our thief!" The man, shocked and guilty, confessed. The villagers were amazed and asked Nasreddin how he knew. Nasreddin chuckled and said, "Simple! The thief didn't dip his finger in the soup because he feared being caught. His finger was the only one not burned by the hot soup!"
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 | 0.011 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.008 | 0.012 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.237 | 0.187 |
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".