Onde está a culpa? (Où est la faille?) HUSTON, Nancy. Lèvres de pierre. Arles: Actes Sud, 2018. Nubia Hanciau (FURG)
Bibliographic record
Abstract
A partir de uma série de semelhanças, Lèvres de pierre de Nancy Huston vai nos mostrar quais são os pontos de convergência entre uma mulher de letras canadense e um certo Saloth Sâr, menino discreto cambojano criado no campo, que por muito tempo faz xixi na cama, ridicularizado por seus irmãos, tornou-se o ditador Pol Pot, conhecido por ter massacrado perto de um milhão de seus concidadãos, responsável pelo maior genocídio do século XX. Em Lèvres de pierre a romancista retraça a trajetória desse “Homem Noite” e as etapas cicatriciais que fabricarão um monstro, ao mesmo tempo que tece o paralelismo entre Dorrit, seu duplo, sua infância e juventude, primeiro em Calgary, depois nos EUA, passando por Paris. Dois seres com contornos frágeis devorados primeiro pelo medo, depois pela raiva.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.007 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.044 | 0.009 |
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".