Bibliographic record
Abstract
On the bridge, however, Roberta felt quite safe, because she could look down on the canal, and if any boy showed signs of meaning to throw coal, she could duck behind the parapet. Presently there was a sound of wheels, which was just what she expected. The wheels were the wheels of the Doctor's dogcart, and in the cart, of course, was the Doctor. He pulled up and called out: “Hullo, head-nurse! Want a lift?” “I wanted to see you,” said Bobbie. “Your mother's not worse, I hope?” said the Doctor. “No — but — “ “Well, step in, then, and we'll go for a drive.” Roberta climbed in and the brown horse was made to turn round — which it did not like at all, for it was looking forward to its tea — I mean its oats. “This is jolly,” said Bobbie, as the dogcart flew along the road by the canal. “We could throw a stone down any one of your three chimneys,” said the Doctor, as they passed the house. “Yes,” said Bobbie, “but you'd have to be a jolly good shot.” “How do you know I'm not?” said the Doctor. “Now, then, what's the trouble?” Bobbie fidgeted with the hook of the driving apron. “Come, out with it,” said the Doctor. “It's rather hard, you see,” said Bobbie, “to out with it; because of what Mother said.” “What did Mother say?” “She said I wasn't to go telling everyone that we're poor. But you aren't everyone, are you?” “Not at all,” said the Doctor cheerfully. “Well?” From: E. Nesbit, The Railway Children (1906), ch. 4
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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 | 0.015 | 0.024 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.021 | 0.066 |
| Scholarly communication | 0.011 | 0.021 |
| Open science | 0.002 | 0.015 |
| Research integrity | 0.010 | 0.016 |
| Insufficient payload (model declined to judge) | 0.014 | 0.002 |
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".