The Better to See You With: Peering into the Story of Little Red Riding Hood, 1695–1939
Bibliographic record
Abstract
I received the 2017 Bechtel Fellowship and spent a month in Gainesville, Florida, from mid-April through mid-May, trekking each day to the University of Florida. There I pored over hundreds of volumes containing the story of Little Red Riding Hood and spent my weekends compiling data or visiting wildlife parks in search of alligators (which were in abundance).The story of Little Red Riding Hood has fascinated me since childhood, and now I am even more intrigued. Intense study of this story has led me to many fine explorations into the tale and has helped me understand the history of children’s book publishing. The Bechtel Fellowship gave me the opportunity to learn a great deal about a specific story, and sharing this knowledge enables me to spread my love of story and children’s books with others. Below is my report from my month of study.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.033 | 0.013 |
| Scholarly communication | 0.007 | 0.007 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.009 | 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".