L’humour dans les romans d’Alain Mabanckou et d’Azouz Begag : de l’autodérision à la singularité
Bibliographic record
Abstract
Si l’humour est destiné à divertir et à amuser, son usage permet également de révéler un point de vue, de critiquer une situation, de se démarquer d’un discours considéré comme conformiste, réactionnaire et inacceptable, et d’interpeler le lecteur dont la complicité active est suscitée par les affirmations et les « non-dit » de l’oeuvre. L’humour qui se déploie dans les oeuvres d’Alain Mabanckou et d’Azouz Begag est particulier à chacun des auteurs ; il constitue leur « marque » d’écriture et souligne ainsi leur singularité d’écrivain. C’est cette singularité, implicitement revendiquée, qui constitue l’ambiguïté de leur discours où sont mises en évidence à la fois une sorte d’autodérision et une autosatisfaction. C’est ce que ces réflexions tenteront de cerner à travers quelques références ponctuelles aux oeuvres des deux écrivains.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.006 | 0.015 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".