John Halifax, Gentleman by <given-names>Dinah Mulock</given-names> <surname>Craik</surname>, <given-names>Lynn M.</given-names> <surname>Alexander</surname>Villette by <given-names>Charlotte</given-names> <surname>Brontë</surname>, <given-names>Kate</given-names> <surname>Lawson</surname>The Woman in White by <given-names>Wilkie</given-names> <surname>Collins</surname>, <given-names>Maria K.</given-names> <surname>Bachman</surname>, <given-names>Don Richard</given-names> <surname>Cox</surname>She: A History of Adventure by <given-names>H. Rider</given-names> <surname>Haggard</surname>, <given-names>Andrew M.</given-names> <surname>Stauffer</surname>The Hound of the Baskervilles with 'The Adventure of the Speckled Band' by <given-names>Arthur Conan</given-names> <surname>Doyle</surname>, <given-names>Francis</given-names> <surname>O'Gorman</surname>'Hauntings' and Other Fantastic Tales by <given-names>Vernon</given-names> <surname>Lee</surname>, <given-names>Catherine</given-names> <surname>Maxwell</surname>, <given-names>Patricia</given-names> <surname>Pulham</surname>My Ãntonia by <given-names>Willa</given-names> <surname>Cather</surname>, <given-names>Janet</given-names> <surname>Sharistanian</surname>The Age of Innocence by <given-names>Edith</given-names> <surname>Wharton</surname>, <given-names>Stephen</given-names> <surname>Orgel</surname>London: A Pilgrimage by <given-names>Blanchard</given-names> <surname>Jerrold</surname>, <given-names>Gustave</given-names> <surname>Doré</surname>, <given-names>Peter</given-names> <surname>Ackroyd</surname> (review)
Bibliographic record
Abstract
No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.011 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.362 | 0.166 |
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".