Bibliographic record
Abstract
Comment lire aujourd’hui Désert , roman-pivot dans l’oeuvre de Le Clézio ? Un quart de siècle après sa publication, cette oeuvre en forme de diptyque continue de fasciner les lecteurs en raison du puissant imaginaire collectif que sollicite une écriture vibrante, chargée d’émotion pure. Ici s’impose la négation par effacement, disparition, brouillage des référents. De façon significative, les évocations sahariennes de Le Clézio sont souvent fondées sur l’observation de déserts américains. Le désert est aussi la terre des révélations, des interrogations absolues. Par-delà la visée démonstrative du texte, l’article s’attache à nuancer les interprétations couramment suscitées par ce lieu fictionnel mythique pour en souligner l’ambivalence profonde qui, loin d’atténuer son pouvoir de fascination, semble au contraire l’exalter. Chaque lecteur peut investir le texte de sa propre méditation sur le Temps, sur l’existence, sur l’origine, parce que l’image du désert demeure celle d’un mystère sur lequel les mots achoppent, mais que le roman exemplifie.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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 teacher head, 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".