The door-scraper in the Wild Wood: an informal lesson in frame metonymy
Bibliographic record
Abstract
(From Chapter 3, The Wind in the Willows , 1908, by Kenneth Grahame) [ The Rat and the Mole are lost in the Wild Wood on a snowy night. As they are slogging through the snow, the Mole cuts his leg. The Rat, intrigued, tries to find the object that hurt the Mole ] Suddenly, the Rat cried “Hooray!” and then “Hooray-oo-ray-oo-ray-oo-ray!” and fell to executing a feeble jig in the snow. “What have you found, Ratty?” asked the Mole, still nursing his leg. “Come and see!” said the delighted Rat, as he jigged on. The Mole hobbled up to the spot and had a good look. “Well,” he said at last, slowly, “I see it right enough. Seen the same sort of thing before, lots of times. Familiar object, I call it. A door-scraper! Well, what of it? Why dance jigs round a door-scraper?” “But don't you see what it means , you – you dull-witted animal?” cried the Rat impatiently. “Of course I see what it means,” replied the Mole. “It simply means that some very careless and forgetful person has left his door-scraper lying about in the middle of the Wild Wood, just where it's sure to trip everybody up. Very thoughtless of him, I call it. When I get home I shall go and complain about it to – to somebody or other, see if I don't!” “O dear! O dear!” cried the Rat, in despair at his obtuseness.
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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 | 0.001 |
| Science and technology studies | 0.002 | 0.009 |
| Scholarly communication | 0.003 | 0.014 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".