Bibliographic record
Abstract
Symbolism is a late-nineteenth-century literary movement centred mostly around the work of poets such as Stéphane Mallarmé, Arthur Rimbaud, Paul Verlaine, Philippe Villiers de L’Isle-Adam, and the later Maurice Maeterlinck, as well as novelists like Joris-Karl Huysmans and Edouard Dujardin. Although Tristan Corbière died in 1875, he is an important figure associated with the movement thanks to his image as a poète maudit (‘poet of the damned’) and to this poetic style. A broad term that occasionally extends to early twentieth-century modernists like T.S. Eliot, James Joyce, and Ezra Pound, Symbolism is traditionally dated from circa 1870 to 1900. (The term ‘Symbolist’ was coined by Jean Moréas in the review La Vogue in 1886.) The movement became more international in the 1890s with the emergence of European Symbolism such as Russian Symbolism, German Symbolism etc., and with poets such as Emile Nelligan in Canada. Of equal importance is its influence as an artistic movement. Symbolism reacted to broader cultural tendencies related to scientific and literary Positivism such as Realism and Naturalism, and the language of the popular press, particularly as it appeared in the form of best-sellers. Where popular language informs the public with moral narratives, Symbolist language tries to avoid such a reduction.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.051 | 0.015 |
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".