Bibliographic record
Abstract
In Revisioning Red Riding Hood around the World, Sandra Beckett presents 52 versions of the tale of Little Red Riding Hood in English translations from 15 other languages, representing authors, illustrators, and publishers from a globetrotter’s list of countries: Argentina, Belgium, Brazil, Canada, Chile, Colombia, Croatia, Cuba, Czech Republic, Ecuador, Egypt, France, Germany, Greece, Italy, Japan, Korea, Lebanon, Mexico, the Netherlands, Norway, Pakistan, Paraguay, Peru, Poland, Portugal, Qatar, Russia, Spain, and Switzerland. While some of the original texts first appeared in the early 20th century, most were published after 1970, and about half of them in the last two decades. While the book has been published by a university press, comments on the back cover of the paperback edition I read suggest it might be appropriate for an audience of students and other less specialized readers as well as for researchers in the field of fairy tales: in these comments, Cristina Bacchilega speaks of the book’s potential appeal to ‘‘a wide English-speaking readership’’ and Vanessa Joosen says, ‘‘What a treasure trove for researchers, what an eye-opener for students, and what a joy to read for all those interested in fairy tales, children’s literature, and world literature.’’ Unlike Bacchilega and Joosen, I am disappointed by this anthology*mostly because, based on their comments and the book’s title, I had such high expectations for it. I was looking forward to what the title identifies as ‘‘an anthology of international retellings’’ of the tale of Red Riding Hood from ‘‘around the world.’’ I wanted to learn how, as Sandra Beckett promises in her introduction, ‘‘the number and diversity of these retellings from the four corners of the globe demonstrate the tale’s remarkable versatility and its unique status in the collective unconscious and in literary culture, even beyond the confines of the Western world’’ (11). Unfortunately, though, both the world this anthology represents and the tales it retells are worrisomely incomplete. There is less here than there ideally ought to be. First, there is less of the world. The globe as depicted here is missing one of its four corners* the one where English is spoken. While Revisioning Red Riding Hood does make a lot of previously unavailable and often interesting material available to the English-speaking parts of the world, it also bypasses the existing English-language context in a way that significantly limits the audience Nordic Journal of ChildLit Aesthetics Vol. 5, 2014
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.004 |
| Scholarly communication | 0.009 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.040 | 0.011 |
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".