Typologizing the Dream / Le rêve du point de vue typologique. Ed. by Bernard Dieterle and Manfred Engel. Würzburg: Königshausen & Neumann 2022 (Cultural Dream Studies; 5) — Contents and Preface
Bibliographic record
Abstract
There is nothing like a firmly established typology of dreams – simply because the taxonomies on which existing typologies are based vary widely: They can be oneirocritical, thematic, or based on dreaming characters or their responses, on narratological functions, etc. The essays in this volume will discuss a broad range of dream types, with a special focus on nightmares and erotic, funny, indigenous and children's dreams. Examples are taken from a great variety of cultures and historical periods. Their authors and artists include: Akinari, Barrie, Baudelaire, Benjamin, Black Elk, Buñuel, Burroughs, W. Busch, Calvino, Cantilo, Cao Xueqin, Cardano, Carroll, Coogler, Corkran, Cortázar, Crébillon fils, Dalí, Eco, Ende, Foer, Fuseli, Garnier, Gatore, Grévin, Grünbein, Guo Moruo, Hauptmann, Hawthorne, Hebbel, Heine, E.T.A. Hoffmann, Huysmans, Ilboudo, Ilibagiza, Kafka, F. Lang, Leiris, Li Yu, Malerba, Mizoguchi, Morgenstern, Mussorgsky, Nodier, Nolan, Okopenko, Pushkin, Radcliffe, Rimbaud, Robison, Schafer, Schiller, Schnitzler, Schwarz-Bart, P.B. Shelley, Soqluman, Storm, Szittya, Tamapima, Tchaikovsky, D.M. Thomas, Tristan L'Hermite, Valenzuela, Vava, Yourcenar, Yu Dafu, and many others
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 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".