Bibliographic record
Abstract
You’re invited to wander the streets of Paris with France’s literary greats. Weaving a remarkable collection from the very best romantic writers, tales of love, loss, and laughter never felt so good.With classics such as Victor Hugo’s ‘The Hunchback of Notre Dame’ and Gaston Leroux ‘The Phantom of the Opera’, you’ll peak behind the scenes at some of the West End’s most remarkable musicals.But it’s not all rosy and bright. In 18th-century France, Charles Dicken’s ‘A Tale of Two Cities’ and Émile Zola’s ‘Nana’ portrays the harsh and raw reality for some of society’s most struggling protagonists. This collection will leave you questioning wealth and worth at a time when struggles were rife.Ideal for fans of ‘Les Misérables’ starring Eddie Redmayne, Hugh Jackman, and Anne Hathaway, this unmissable collection is a must-read for French history, art, and culture fanatics.Gaston Leroux (1868-1927) was a French journalist and author of detective fiction. He is best known for writing the epic novel ‘The Phantom of the Opera’, now a musical masterpiece by Andrew Lloyd Weber.Gustave Flaubert (1821-1880) was a French novelist and pioneer of literary realism. His work spans ‘Madame Bovary’, ‘Sentimental Education’, and ‘Three Tales’.Victor Hugo (1802 1885) was a Romantic writer and politician. Celebrated for his internationally renowned epic novel, adapted into the Academy Award-Winning film, ‘Les Misérables’, his literary output also includes ‘The Hunchback of Notre Dame’.Émile Zola (1840-1902) was a French novelist, playwright, and one of the most influential writers of French naturalism. Zola’s best-known works include ‘Germinal’, ‘Nana’, and ‘Work’.Celebrated as one of the greatest novelists of the Victorian era, Charles Dickens (1812-1870) was an English writer and social critic. His works include ‘Oliver Twist’, ‘A Christmas Carol’, and ‘Great Expectations’.
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 | 0.003 | 0.005 |
| Science and technology studies | 0.016 | 0.011 |
| Scholarly communication | 0.021 | 0.005 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.117 | 0.020 |
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".