Bibliographic record
Abstract
Introduction: The Compulsion to Repeat Mavis Reimer, Nyala Ali, Deanna England, and Melanie Dennis Unrau 1. Off to See the Wizard Again and Again Laurie Langbauer 2. 'Anne repeated': Taking Anne Out of Order Laura M. Robinson 3. Kierkegaard's Repetition and the Reading Pleasures of Repetition in Diana Wynne Jones's Howl's Moving Castle Series Rose Lovell-Smith 4. Harry Potter Fans Discover the Pleasures of Transfiguration Eliza T. Dresang and Kathleen Campana 5. Girls, Animals, Fear, and the Iterative Force of the National Pack: Reading the Dear Canada Series Charlie Peters 6. 'But what is his country?': Producing Australian Identity through Repetition in the Victorian School Paper, 1896-1918 Michelle J. Smith 7. Serializing Scholarship: (Re)Producing Girlhood in Atalanta Kristine Moruzi 8. 'I will not / be haunted / by myself!': Originality, Derivation, and the Hauntology of the Superhero Comic Brandon Christopher 9. Michael Yahgulanaas's Red and the Structures of Sequential Art Perry Nodelman 10. The Beloved That Does Not Bite: Genre, Myth, and Repetition in Buffy the Vampire Slayer Debra Dudek 11. Roy and the Wimp: The Nature of an Aesthetic of Unfinish Margaret Mackey 12. MP3 as Contentious Message: When Infinite Repetition Fuses with the Acoustic Sphere Larissa Wodtke 13. The Little Transgender Mermaid: A Shape-Shifting Tale Nat Hurley Index
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.008 | 0.008 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".