Bibliographic record
Abstract
’ Kay bayho ?’ (What’s the problem?) I ask, smiling at the child before me, who has purple cheeks and mucus running down his upper lip. He looks back at me blankly: he is only 5 years old and, although I was using Nepali, his mother tongue is Gurung, one of 93 native languages spoken in Nepal. Home for this little boy is quite literally at the roof of the world in Upper Mustang, on the Sino-Nepalese border. For 6 months of the year it is too cold to study at over 4000 m and, with the rest of his school, he has made a 2-day journey by jeep and bus down to Pokhara at 825 m, where he is spending winter in a residential school. On the morning commute in Pokhara you see dozens of school-bound children. Pretty girls with their shiny black plaits tied in ribbons to match their spotless tights, boys in navy blazers and flannel trousers, and toddlers in brightly coloured jumpers with beanie hats. These are the children who greet you on the street with a polite ‘ Namaste ’ or, giggling shyly, try …
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.010 | 0.010 |
| Insufficient payload (model declined to judge) | 0.063 | 0.019 |
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".