KITTENS AND KITCHENS: FOOD, GENDER, AND<i>THE TALE OF SAMUEL WHISKERS</i>
Bibliographic record
Abstract
With the publication ofThe Tale of Mr. Tod(1912), Beatrix Potter articulated her impatience with “goody goody books about nice people” (Linder 210) and declared her intention “to make a story about two disagreeable people” (Potter,Tale of Mr. Tod7). Yet although the subjects of the story, Tommy Brock and Mr. Tod, might be the most viciously disagreeable protagonists in Potter's children's stories, her readers were already acquainted with characters who challenged the boundaries of propriety, graciousness, and respectful deference to authority. Throughout her oeuvre, many such characters are not entirely punished for their trespasses, a pattern which often surprises modern readers who blithely assume that the daintily-illustrated books about woodland critters and barnyard creatures affirm conservative Edwardian conventions of behavior and standards of decorum.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.021 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".