L’avenir des sabliers. Sur des poèmes de Sélim Baghli et de Farid Laroussi
Bibliographic record
Abstract
Autant la poésie de Sélim Baghli peut être hachée, hésitante, inquiète d’elle-même, autant celle de Farid Laroussi frappe d’estoc, est assurée et ferme. Quand les vers de Baghli cooptent Verlaine, la prose de Laroussi affiche plus d’une accointance avec celle de Rimbaud. Cet article ne se propose pas de produire un brillant paradoxe à la manière des critiques littéraires vaporeux, mais d’essayer de comprendre comment des poèmes de Baghli et de Laroussi, en mettant à leur manière en scène un sujet qui résiste aux ravages du passé tout en étant dans le temps de l’après-coup, se trouvent plus ou moins travaillés par le thème de « l’Algérie malgré tout ».As much as Sélim Baghli’s poetry can be choppy, faltering, and self-conscious, Farid Laroussi’s is direct, self-assured and firm. Where Baghli’s verses co-opt Verlaine, Laroussi’s prose has a clear link to Rimbaud. This article does not attempt to illuminate an obvious paradox in the hazy style of literary critique, but instead it attempts to understand how the poems of Baghli and Laroussi portray, in their own way, a subject that resists the ravages of the past all the while existing in the post-coup era, finding themselves more or less shaped by the theme of “Algeria in spite of it all.”
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.008 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".