DOUNIA, ENTRE NOSTALGIE ET MÉLANCOLIE ("LE BONHEUR A LA QUEUE GLISSANTE" D’ABLA FARHOUD)
Bibliographic record
Abstract
Dounia, between nostalgia and melancholia (Le Bonheur a la queue glissante d’Abla Farhoud). The aim of this article is to analyze the novel Le Bonheur a la queue glissante written by Abla Farhoud (a Canadian writer of Lebanese origins) by following a two-way sentimental pattern: nostalgia and melancholia. We are of the opinion that these feelings are an indelible characteristic of this autobiographic novel which tells us the story of a Lebanese woman who has always lived with the feeling of being an eternal immigrant. Nevertheless, the writing process, which is a nostalgic one aimed to recover a lost paradise, cannot be understood as part of melancholia. Regarding the latter, we further analyze its sources and ways of manifestation. REZUMAT. Dounia, între nostalgie și melancolie (Le Bonheur a la queue glissante d’Abla Farhoud). În acest articol ne propunem să analizăm romanul Le Bonheur a la queue glissante de Abla Farhoud (scriitoare canadiancă de origine libaneză) după o grilă cu două intrări « sentimentale » : nostalgia și melancolia. Aceste sentimente constitue, după părerea noastră, marca individualizatoare a acestui roman autobiografic care descrie melancolia resimțită de o femeie de origine libaneză care s-a simțit întodeauna o eternă imigrantă. Actul scrierii, act nostalgic de recuperare al unui paradis pierdut, nu poate fi deloc înțeles fără această compozantă, melancolia, ale carei surse și forme de manifestare sunt obiectivul analizei noastre. Cuvinte-cheie: Abla Farhoud, nostalgie, melancolie, roman biografic, scriitură migrantă
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.008 | 0.007 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".