Bibliographic record
Abstract
Julia Schabas’ lovely essay on “Exploring the Unsolvable in Margaret Atwood’s ‘Bluebeard’s Egg’” recognizes the mystery that lies in the old fairy tales and the way that mystery engages contemporary writers. The tales were widely circulated and retold because of some inexplicable attractive mystery to listener or reader. This mystery was first critically analysed by Romantics like Schlegel and Novalis. As the latter said: “In a genuine fairy tale everything must be incoherent.” But for them, and for modern writers like Atwood, this apparent incoherence of meaning was a call to the reader to find some kind of higher order coherence or insight. That is what Julia Schabas finds carried over by Atwood from the older tale of “Bluebeard”: the unpredictable and inexplicable shifting power politics in an uneasy marriage are now enhanced and opened up to us in all their contradictions. —Dr. William Barker
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 | 0.010 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.016 | 0.020 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.050 | 0.046 |
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".