Bibliographic record
Abstract
This special issue of Marvels & Tales includes papers that were presented at a conference hosted by the Cotsen Children's Library at Princeton University on 30-31 March 2001. That memorable conference—organized by Andrea Immel, U. C. Knoepflmacher, and Jan Susina—brought together fairy-tale scholars from Canada, Germany, Sweden, and the United States to engage the program's theme—Considering the Kunstmärchen: The History and Development of Literary Fairy Tales— from a variety perspectives. The conference not only bore witness to the good sense in considering and re-considering the literary fairy tale in light of the developments that have occurred in fairy-tale scholarship over the last three decades (and in particular since the Princeton University conference on Fairy Tales and Society in 1984); it also demonstrated the vitality of fairy-tale studies itself as a coherent field of research—a field that exemplifies not simply the value, but also the very necessity of cross-cultural and interdisciplinary perspectives and conversations. For this reason, there could be no better forum for the publication of these important papers than Marvels & Tales: Journal of Fairy-Tale Studies. The editors of Marvels & Tales are grateful to Andrea Immel and Jan Susina for serving as the Guest Editors of this special issue.
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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 | 0.002 | 0.014 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.329 | 0.265 |
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".