BETWEEN PAINTING AND MUSIC: THE REPETITION IN THE WORK OF CLARICE LISPECTOR
Bibliographic record
Abstract
Considering that the work of the writer Clarice Lispector is composed of fragments of texts published almost everywhere: her novels are constructed from extracts from the stories, the stories are reissues of newspaper columns, the newspaper columns form another novel, etc., we will try to demonstrate with this article that such repetition, the insistence on repeating the same words, the same ideas, the same characters, ends up creating a unique literary style which constrains us, by force, to repeat, to open your eyes and see. With repetition, we manage to see what is always there, what is always given, obvious, quotidian. And since repetition is poorly accepted in literature, as Hélène Cixous underlines, we will see that Lispector uses painting and music to support her “repetitive” work and reveal that literary repetition, between painting and music, can bring out the “true” thing and lead us towards the prehistory of a future.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.014 | 0.019 |
| Scholarly communication | 0.009 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".