Bibliographic record
Abstract
‘The custom, in periodicals, of sustaining interest by happily-conceived divisions of the plot, may perhaps be traced to this subtle artifice of Scheherazade’, James Mew observes in the Cornhill Magazine in 1875. 1 Mew was not the first writer to make the analogy between the skills of the fairytale narrator and those of the magazine novelist: for Dickens, Mrs Gaskell was famously ‘my Scheherazade’, spinning artfully rationed weekly instalments for the readers of Household Words . 2 In fact, both echoed enterprising editors from the previous century, who made literal links between Scheherazade and serialisation. While the first English novel serialised in a newspaper, Daniel Defoe’s Robinson Crusoe , was still running in the Original London Post , the Churchman’s Last Shift was entertaining its readers with ‘The Voyages of Sinbad’. Three years later, in 1723, the Arabian Nights began appearing thrice-weekly in Parker’s London News . Making good use of its heroine’s mastery of pleasurable postponement, the serialisation took over three years to come to completion. 3 These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.007 | 0.014 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.018 | 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".