Bibliographic record
Abstract
[EN] The most common element in the novels, essays and autobiographical texts by Marguerite Yourcenar is \nby far this aqua permanens that keeps us alive as a constituent part of our body, the primitive water that \ncovered « the essential and the unlimited » the funeral water of the Nile where the young Antinous \nvoluntarily drowns, the one, fallen from heaven, which governs the attempted murder of Marcella in the \nperson of the dictator, the one, singing, from the Trevi Fountain, the water that purifies Zeno when \nbathing on the beach at Heyst, that of the sea raging on the Frisian island where Nathanael agonises, devastated by the water that fills his lungs, that of the Canadian rivers covered by the author, or even, the \nwaters that resonate in the ears of Zeno, at the time of his death. The writer, accustomed from childhood \nto the loose wetlands in the North, to the beaches where she used to play with her friends, to the cruises, \nto the transatlantic journeys, to the contemplation of water in all its forms, the liquid element is one of the \npillars of her imaginary. Through an analysis of these symbolic images that abound in her writings, \nespecially in those of her maturity and the end of her life, we will try to show the richness and variety of \nrepresentations of water and their function in a large part of Yourcenar’s literary work.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.013 | 0.014 |
| Scholarly communication | 0.009 | 0.004 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.020 | 0.004 |
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".